Business Statistics And Sales Revenue Forecast University Essay Example

Introduction

Forecasting future sales with certainty is impossible since the market conditions are unpredictable. The economy is always constantly changing. Despite the uncertainties, management of companies still need to prepare sales forecasts to enable them to prepare a budget for revenue and expenses. The preparation of such budgets is always based on past trends and future objectives of the company. There are serious implications when a company does not achieve its targets. For instance, when the forecasted revenue and net income falls way below forecasted revenue, the share prices of the company are likely to fall. Thus, the management needs to come up with real values when preparing the budgets. This can be achieved by coming up with models that will help estimate the future values of sales revenue. Examples of such models are regression models. The regression models can either be linear or polynomial. Past trends are used to model a regression line. After that, the regression line can be used to estimate future trends. Estimating future values can also be achieved by progressing current values by the proportion of the estimated growth rate of various components of the budgets. The method is commonly used for new business.

Purpose and objectives

This paper aims to carry out an analysis of sales revenue for a company. The analysis will entail coming up with regression models to aid in forecasting the sales revenue of the company for the year 2005 and 2006.

Methods

Historical sales data for the company is provided for five years, that is, between 2000 and 2004. The data is arranged quarterly. The analysis of the data will entail observing the trend of the sales revenue data for the past five years to come with a model that can be used to estimate sales revenue values of the company between 2005 and 2006. There are several methods that can be used to forecast sales revenue. Most methods aim at giving the trend of sales revenue for previous years. After that, a model is estimated to show the relationship between various values. The models generated are tested to ascertain if they are suitable for estimating future values. After that, such models can then be used for future sales revenue. Some of the forecasting models that are commonly used are a linear trend, nonlinear trend and exponential forecasting. Linear forecasting is often preferred since it is timely, accurate, reliable, meaningful, and easy to use. Also, moving averages are often computed to give an overall impression of the pattern of movement of sales revenue over time. Thus they are known as smoothing techniques. In this analysis, regression will be carried to establish a linear relationship between the sales revenue of the company and period time. This linear relationship will be used to forecast sales revenue of the company for the year 2005 and 2006. In addition, graphs and charts will be used to display various trends of the values computed and relationships of various variables. Excel will be used to carry out various computations and analysis.

Analysis

This section comprises of an extensive review of the sales revenue data provided. Some of the areas that will be discussed below are an analysis of the original data, correlation coefficient, linear regression model, variations in the data, analysis of various components of the data, prediction of values, testing the regression model estimated and forecasting the sales for 2005 and 2006. Various statistical tools will be used to reviewing the data provided and coming with a sales forecast.

Analysis of the original data

As mentioned above, the sales revenue data is provided for five years, that is between 2000 and 2004. The data are arranged in quarters. The table below shows the historical data of sales revenue for the company for various periods.

Historical Sales Revenues
Year Quarter Period Sales
2000 1 1 $684.20
2000 2 2 $584.10
2000 3 3 $765.40
2000 4 4 $892.30
2001 1 5 $885.40
2001 2 6 $677.00
2001 3 7 $1,006.60
2001 4 8 $1,122.10
2002 1 9 $1,163.40
2002 2 10 $993.20
2002 3 11 $1,312.50
2002 4 12 $1,545.30
2003 1 13 $1,596.20
2003 2 14 $1,260.40
2003 3 15 $1,735.20
2003 4 16 $2,029.70
2004 1 17 $2,107.80
2004 2 18 $1,650.30
2004 3 19 $2,304.40
2004 4 20 $2,639.40

The table shows that there has been a general increase in the sales revenue for the company for five years. In the first quarter of 2000, the total amount of sales revenue increased from $684.20. The value increased to $2,639.40 in the last quarter of 2004. It can be observed that the company recorded a decline in sales in the second quarter of all four years. For instance, in 2000, the sales declined from $684.20 in the first quarter to $584.10 in the second quarter. Similarly, in 2001, the sales declined from $885.40 in the first quarter to $677.00 in the second quarter. The same trend was observed in 2002. The number of sales declined from $1,163.10 in the first quarter to $993.20 in the second quarter. Also, in 2003, the number of sales declined from $1,596.20 in the first quarter to $1,260.40 in the second quarter. Finally, in 2004, the number of sales declined from $2,107.80 in the first quarter to $1,650.30 in the second quarter. There was an increase in sales for the third and fourth quarter for all the years. The graph below shows the trend in sales revenue for the 20 periods in the five years.

Sales Revenue
Sales Revenue

Correlation coefficient

The correlation coefficient measures the degree of relationship between two variables. A positive value of correlation coefficient implies that the variables being investigated have a positive relationship that is, an increase in one value leads to a decline in the other variable. On the other hand, a negative value implies that the variables have a negative relationship. That is, they move in opposite directions. As the value of one variable increases, the value of the other variable declines. A high value of the correlation coefficient shows that the values have a strong relationship. The correlation coefficient between sales revenue and time is 0.9341. This implies that there is a strong positive relationship between sales revenue and time. That is, sales revenue increases over time, as shown in the scatter diagram below.

Sales Revenue over time
Sales Revenue over time

Linear regression model

Regression analysis is a statistical tool that is used to develop approximate linear relationships among various variables. Regression analysis formulates an association between several variables. There are several variables that can affect the sales revenue of a company. Some of the factors are price charged for the products of the company, competition, news about the company, and the amount spent on advertising, among other factors. When more than one factor is used when coming up with the regression equation, then a multiple regression analysis will be used. However, in this case, a simple regression will be used. When coming up with the simple linear regression model, it is necessary to separate between dependent and independent variables. The dependent variable is the sales revenue, while the independent variable is the period.

The regression line will take the form Y = b0 + b1X

Y = Sales revenue

X = Time

The theoretical expectations are b0 can take any value and b1 > 0 (positive).

Regression Results

Variable Coefficients of the variable
b0 Y-intercept 375.1758
X Period 92.62564

From the above table, the regression equation can be written as Y = 375.1758 + 92.62564X. The intercept value of 375.1758 represents other variables that affect the average sales revenue but are not included in the modelling. The coefficient value of 92.62564 implies that as time increases by one unit, the sales revenue increases by 92.6256 units. When the regression equation is compared with the scatter diagram, there is an indication of consistency. The graph of average sales revenue and time shows an upward trend with a correlation coefficient of 0.9341. The regression equation above also yields a positive slope. Thus, it is clear that the regression equation is sensible. The graph below shows the graph of the original plots of sales revenue and time with a line of best fit.

The graph of the original plots of sales revenue and time with a line of best fit
The graph of the original plots of sales revenue and time with a line of best fit

Variation of the sales data

Measurement of variation of sales revenue data provided is of utmost importance. It shows the extent to which the data for sales revenue will deviate from the average. There are several ways of measuring the extent of variation in the amount of sales revenue. A commonly used method is by computing the variance and standard deviation of a stock. The two approaches measure the volatility of sales revenue. High variance and standard deviation indicate a high variation from the mean value. A low value of variance and the standard deviation is suitable since it shows low variation from the mean. Another tool that is often used to measure variation is the coefficient of variation. It is the ratio of mean and standard deviation. The ratio is regarded as superior since it brings the average of the asset being discussed in context when analyzing the standard deviation of the asset. The table below summarizes the value of variance, standard deviation, and coefficient of variation of sales revenue.

Item Value
1 Mean 1347.745
2 Variance 344,118
3 Standard deviation 586.6157
4 Coefficient of variation 2.297492

The variance, standard deviation and coefficient variation of the sales revenue are quite high. This implies that there is a high variation from the average sales value of $1,347.75. It implies that the sales value can go as low as $761.62 and as high as $1,934.361. The high variations weaken the regression model that will be used for estimating future values. It is because high variation results in a higher value of the residual sum of squares. This creates the need for smoothing the data using moving averages.

Comparisons of yearly sales data

The section will compare the sales data for the four quarters of the five years. The graph below shows the trend of values.

Comparisons of yearly sales data
Comparisons of yearly sales data

There is an increase in sales revenue over time. The sales data for 2004 are higher than those for the earlier years. From the graph, it is also evident that the sales revenue in all the years increased with a sharp decline in the second quarter. Thus, it is evident that there is a general increase in sales revenue within the quarters of all the years.

Trend and ratios

The trend values are obtained by using the regression line to estimate the value of the sales revenue. Ratios of original and fitted sales revenue data are obtained. The graph of the ratios per quarter is different from the graph of the original sales data per quarter. The gap in years closes in when ratios are used. The values for all the years declined from the first quarter to the second quarter. After that, there is an increase but at a slower rate and the graphs tend to close into a common point.

Ratio of sales revenue to trend
Ratio of sales revenue to trend

The table below summarizes the average ratios for each quarter.

Quarter Average ratios
First 1.114585199
Second 0.818898515
Third 1.031927719
Fourth 1.103812955

The average ratios above display the general trend of the ratios for each of the years. It displays the same trend as discussed above. They declined from the first quarter to the second quarter and after that an increase. This can be displayed in a graph illustrated below.

Average ratios
Average ratios

Prediction of values

The regression model estimate above, that is, Y = 375.1758 + 92.62564X can be used to predict future values of sales revenue. Such model gives an estimation of the values and not the exact values. However, before the model can be used for prediction, it is necessary to evaluate if it is suitable and can be used for prediction. The two criteria that will be used for prediction are the coefficient of determination and testing the statistical significance of the variable.

Coefficient of determination

Coefficient of determination estimates the number of variations of the dependent variable explained by the independent variables. A high coefficient of determination implies that the explanatory variables adequately explain variations the sales revenue. A low value of the coefficient of determination implies that the explanatory variables do not explain the variations in sales revenue adequately. For this regression, the value of R2 is 87.26%. This implies that time period explains 87.26% of the variation in the price of houses. It is an indication of a strong explanatory variable. Also, the value of adjusted R2 is high at 86.55%. Thus, the regression model is suitable and can be used for prediction.

Testing the statistical significance

Testing statistical significance shows whether the explanatory variable is a significant determinant of the price of houses. A t-test will be used since the sample size is small. A two-tailed t-test is carried out at a 95% level of confidence.

Null hypothesis: Ho: bi = 0

Alternative hypothesis: Ho: bi ≠ 0

The table below summarizes the results of the t-tests.

Variable t – values computed t at α 0.05 Decision
b0 Intercept 3.754635 1.9432 Reject
X1 Period 11.10427 1.9432 Reject

The null hypothesis implies that the variables are not significant determinants of demand. The alternative hypothesis implies that variables are a significant determinant of demand. From the table above, the values of t – calculated are greater than the values of t – tabulated. Therefore, the null hypothesis will be rejected, and this implies that the period is a significant determinant of the explanatory variable at the 95% level of significance. The value of the intercept is not relevant when testing the significance of the regression variables. Since the explanatory variable is statistically significant, it implies that the regression line can be used for prediction.

Other tests that can be carried out are tests for multicollinearity, autocorrelation and linearity, among others. From the two tests conducted above, it is evident that the regression model is strong enough and can be used to predict future values of sales revenue. The values predicted will be a continuation of the straight line shown in the graph below.

The values predicted will be a continuation of the straight line
The values predicted will be a continuation of the straight line

The graph below shows the trend of trend and ratios that can be used for prediction.

The trend of trend and ratios that can be used for prediction
The trend of trend and ratios that can be used for prediction

From the above analysis, the regression model can be used for estimation. Thus, the estimated values for sales for the year 2005 and 2006 are shown in the table below.

Year Quarter Period Trend Forecast
2005 1 21 2320.314 2586.188
2005 2 22 2412.94 1975.953
2005 3 23 2505.565 2585.562
2005 4 24 2598.191 2867.917
2006 1 25 2690.817 2999.145
2006 2 26 2783.442 2279.357
2006 3 27 2876.068 2967.894
2006 4 28 2968.694 3276.883

From the graph above, the forecast shows that there will be an increase in sales for 2005 and 2006. The forecasts for the two years will follow the same trend as the previous years. That is, a decline from quarter one to quarter two and an increase thereafter, as shown in the graph below.

Decline from quarter one to quarter two and an increase thereafter
Decline from quarter one to quarter two and an increase thereafter

Vaginitis Diagnosis: Discussion And Analysis

Vaginitis is a common medical condition among women in the reproductive age group and is responsible for numerous visits to healthcare facilities and much distress due to its capacity to negatively impact the overall quality of life (Guedou et al., 2013). Although vaginitis is basically the most common gynecologic diagnosis encountered by health care professionals who provide medical services to women, achieving an accurate diagnosis of the genitourinary disorder can be elusive due to its many variants (Egan & Lipsky, 2002). This paper reviews a case study based on one of the variants of vaginitis known as bacterial vaginosis.

Subjective Data

The main symptoms presented by the 28-year-old female patient include burning and pain upon urination, elevated lower abdominal pain, history of urinary tract infections (UTIs), and brown foul-smelling vaginal discharge after engaging in unprotected sex. It is also clear that the patient has experienced three UTIs in one year and has also been infected with gonorrhea (two times in one year) and Chlamydia (one time). She underwent tubal ligation two years ago and did her last pap smear six months ago, which turned out positive for dark-looking urine. She has a history of multiple male sexual partners and she has been on medication for UTIs.

Objective Data

Her blood pressure (100/80) is indicative of prehypertension, though her heart rate (80) appears to be within the normal range. Other physical examinations (RR 16; T 99.7F; Wt 120; Ht 5’ 0”; HEENT WNL; Chest WNL; Rectal WNL; and Cardio) appear unremarkable except for the softness and tenderness of the abdominal area, cervical motion softness, adnexal tenderness, as well as foul smelling vaginal discharge. Laboratory results are as follows: “Lkc differential: Neutraphils 68%, Bands 7%, Lymphs 13%, Monos 8%, EOS 2%”; “UA: Straw colored. Sp gr 1.015, pH 8.0, Protein neg, Glocuse neg, Ketones neg, Bacteria – many, Lkcs 10-15, RBC 0-1”; “Urine gram stain – gram negative rods”; “Vaginal discharge culture – Gram negative diplococci, Nesisseria gonorrhoeae, sensitivities pending”, and “Positive monoclonal AB for Chlamydia, KOH preparation, Wet preparation, and VDRL negative” (Class Case Study).

Assessment

The three priority diagnoses arising from the subjective and objective data include the variants of vaginitis known as Trichomonas, cervicitis, and bacterial vaginosis. The reason for choosing Trichomonas Vaginalis is because of the high pH, the presence of smelly vaginal discharge, genital burning, as well as pain during urination and sexual intercourse (Nenadic & Pavlovic, 2015).

Similarly, the reason for choosing cervicitis is because of the purulent vaginal discharge, the high pH, and the gram stain test revealing gram negative diplococci with the presence of Nesisseria gonorrhea (Guedou et al., 2013). Lastly, the reason for making a diagnosis for bacterial vaginosis is because of the smelly vaginal discharge, pain during sexual intercourse and/or urination, pH greater than 4.5, and the gram stain test revealing gram negative diplococci indicative of Nesisseria gonorrhoeae (Nenadic & Pavlovic, 2015).

Plan of Care

The preferred treatments for bacterial vaginosis include “metronidazole 500 mg orally twice a day for 7 days, metronidazole gel intravaginally once a day for 5 days, or clindamycin cream intravaginally at bedtime for 7 days” (Campos-Outcalt, 2011, p. 143). It is also important to educate and counsel the patient not to douche and to minimize the utilization of shower gel, antiseptic agents or shampoo in the bath (Williams, 2005), not to prolong the use of topical vaginal gels as they can contribute to a sustained duration of vaginal symptoms and potential damage to the vulvar epithelium (Theroux, 2005), and not to self-treat the symptoms but to seek medical assistance in case of recurrence (Nenadic & Pavlovic, 2015).

Evaluation of Priority Diagnosis

The most likely diagnosis is bacterial vaginosis as the following criteria for the disorder has been satisfied; pH of more than 4.5; clue cells more than 20%; homogenous vaginal discharge; and positive whiff test (amine odor with addition of KOH) (Alfonsi, Shlay, & Parker, 2004).The disease lowers the quality of life of patients due to the discomfort it causes and the smelly vaginal discharge (Guedou et al., 2013). The patient should avoid having sexual intercourse when applying the vaginal treatment gels because of the annoying vaginal symptoms.

Furthermore, it is important to share the knowledge that the disorder “is associated with increased risks of more serious conditions such as pelvic inflammatory disease (PID), postoperative infections, and pregnancy-related complications including prematurity” (French, Horton, & Matousek, 2004, p. 806). Lastly, it is important to discuss with the patient the importance of using protection during sexual intercourse because the disorder increases the probability of acquiring HIV once a woman is exposed to the virus. An interdisciplinary team comprising a nurse (to provide education and information), a social worker (to monitor medication adherence and lifestyle change), and a counselor (to provide advice of sexual choices) can provide the needed care to achieve optimum disorder management and outcomes.

Facilitators and Barriers

In facilitators, the patient needs to follow the treatment regimen and develop alternative strategies to douching. In barriers, the patient should know that it is impossible to achieve optimal disorder management if she continues having multiple sexual partners and if she continues experiencing recurrent UTIs (Nenadic & Pavlovic, 2015). The condition can also be worsened by cigarette smoking, use of intrauterine device, having sex with another woman, and failure to use a condom during sexual intercourse (Alfonsi et al., 2004).

Conclusion

This paper has reviewed a clinical case study based on one of the variants of vaginitis known as bacterial vaginosis. From the discussion and analysis, it is clear that there exists a need for patients to visit qualified healthcare professionals so that a correct diagnosis is made due to similar symptoms in a number of genitourinary disorders under vaginitis.

References

Alfonsi, G.A., Shlay, J.C., & Parker, S. (2004). What is the best approach for managing recurrent bacterial vaginosis? Journal of Family Practice, 53(8), 650-652.

Campos-Outcalt, D. (2011). CDC update: Guidelines for treating STDs. Journal of Family Practice, 60(3), 143-146.

Egan, M., & Lipsky, M.S. (2002). Vaginitis: Case reports and brief review. AIDS Patient Care and STDs, 16(8), 367-373. doi: 10.1089/10872910260196396

French, L., Horton, J., & Matousek, M. (2004). Abnormal vaginal discharge: Using office diagnostic testing more effectively. Journal of Family Practice, 53(10), 805-814.

Guedou, F.A., Van Damme, L., Deese, J., Crucitti, T., Becker, M., Mirembe, F.,…Alary, M. (2013). Behavioral and medical predictors of bacterial vaginosis recurrence among female sex workers: Longitudinal analysis from a randomized controlled trial. BMC Infectious Diseases, 13(1), 1-11. doi: 10.1186/1471-2334-13-208

Nenadic, D., & Pavlovic, M.D. (2015). Value of bacterial culture of vaginal swabs in diagnosis of vaginal infections. Military Medical & Pharmaceutical Journal of Serbia, 72(6), 523-528. doi: 10.2298/VSP140602061N

Theroux, R. (2005). Factors influencing women’s decision to self-treat vaginal symptoms. Journal of the American Academy of Nurse Practitioners, 17(4), 156-162. doi: 10.1111/j.1041-2972.2005.0024.x

Williams, O. (2005). Bacterial vaginosis: Don’t miss the most common cause of vaginal discharge. Pulse, 65(24), 72-76.

“Grief” A Poem By Elizabeth Barrett Browning

Writing about poems is always interesting and challenging. The analysis of Elizabeth Barrett Browning’s poem is not an exception. On the one hand, it is a real challenge to understand the meaning of each word in the poem and the intentions of the writer. On the other hand, it is a real pleasure to read every new line of the poem and open new images and attitudes to such words as “grief”, “despair”, and “death”. The style of Barrett Browning’s writing is impressive indeed. It is complicated because the author tries to use the complicated combinations of words, figurative language such as imagery and simile, and sounds effects in the form of a properly chosen rhyme. “Grief” symbolizes a guide for those, who experience grief, explains the possible types of grief, and the necessity to deal with its regardless the method chosen; it is the poem where Barrett Browning shares her feeling, emotions, and knowledge of how to teach and support the reader at the same time.

To comprehend the essence of her poems, it is very important to learn several facts from the Barrett Browning’s life and identify the main topics she wanted to cover in her 14-lines poem. Elizabeth Barrett Browning was born in England in 1806. When she was 12, she introduced her first impressive poem. In the middle of the 1800s, she was one of the most popular writers in England and in the whole world. Her poems were read by people from different parts of the world. Her ability to combine her personal experiences and express them on paper impressed millions of people. Her tone and the use of words made people think about the importance of education in order to comprehend a number of simple things that are around. Grief is the emotion that could be expressed by any person because of different reasons. Barrett Browning offers her own vision of grief, the situations when grief cannot be avoided, and the ways of how people could survive it.

There are three main aspects that have to be taken into consideration during the analysis of the poem: diction, imagery and figurative language and sound effects and forms. Each aspect has its own goal and consequence to the reader. It is not necessary to compare them or to find out their weak or strong points. It is enough to read and learn how the poem “Grief” could penetrate readers’ souls.

Diction in poems evaluates the use of words and the manner of speaking to the reader. The peculiar feature of the Barrett Browning’s poem is her intention to share her personal loss and help people, who could experience the same to cope with the challenge and take only the best moments in a future life. Barrett Browning says that grief is the center of her poem. According to the American Heritage Dictionary, grief is a type of mental anguish, frustration, or difficulty to cope with the loss. However, it is wrong to believe that if Barrett Browning titled her poem as “Grief”, it turns out to be the only issue for consideration.

In the poem, there are several other crucial words that have to be underlined. For example, much attention could be paid to the last word in the second line, “despair”. It seems like the author wants to compare “grief” and “despair” and improve the explanations of these two words introducing them as synonyms. Besides, there is another word that is taken from the Dictionary and used by Barrett Browning, “anguish”. From the first lines of the poem, it becomes clear that the author tries to impose as many negative and painful words on grief as possible. There is no place for compassion, understanding, or happiness. Still, there is always some place for “silence” that could be used as the solution to “grief”. Besides, silence is not only “grief for thy dead”. Silence is also the synonym for death that stands as a “monumental statue” eternally and “motionless”. In fact, the use of words and the attempts to transfer a lot of meanings in 14 lines are impressive indeed. The depth and sincerity of the author could bribe the reader and make them cry as soon as the last line is read.

Imagery and figurative language are the two main elements used in the poem “Grief”. A simile is the type of figurative language that is usually characterized by the presence of such words as “like” or “as”. Both of them are used in the poem. Barrett Browning compares “souls as countries” and silence to death and “monumental statue”. Besides, there are many metaphors in the poem because the author wants to underline the impact of grief on human lives and the impossibility to avoid it as it was alive and spread through the “midnight air”. Finally, it is impossible to omit the presence of imagery in the work when the author wants to present objects and actions considering human physical actions. The examples of this technique could be found in the fourth, seventh, and eighth lines of the poem. All these techniques and successful comparison make the reader believe that the author knew a lot about grief and experienced it to a certain extent so that she could offer such explanations.

Finally, sound effects and form are the tools that make “Grief” a powerful poem. Of course, the reader cannot guess the tone or the loudness of each word Barrett Browning chose for her poem. Still, sound qualities and effects could be evaluated with the help of rhyme and forms offered. First of all, the attention is paid to the fact that Barrett Browning used the Petrarchan type of sonnet, ABBAABBACDECDE. There are two parts of the poem: the first is an octave with A and B rhymes repeating in a certain order, and the second is a sestet with C, D, and E rhyme repeating in a rotation. The measurement of rhythmic quantity, also known as metrical lines or meter, has its effect on the poem. Though each line has another line with a similar ending, the readers are able to choose their own manner of reading and make the pauses when they find them appropriate. Barrett Browning. Besides, it is necessary not to forget that poetry is the place when a number of near rhymes occur. It means that the authors could use the words with slant rhymes. In the poem by Barrett Browning, each word seems to be its own place, no mistakes or contradictions.

In addition to all those tools and techniques used by the author, it is also necessary to remember the goals set while creating the poem. Barrett Browning wrote the poem after the death of her beloved brother. She was broken and unable to write for some time. She could not accept the truth and was in search for a new solution. It was difficult for her to understand that she could do nothing with the event that happened, and everything she had to do was to write about her feelings and help people, who could experience the same or similar grief. It is not easy to lose beloved people and be able to give some pieces of advice. Barrett Browning proved that her writing skills, abilities to use figurative language and imagery, and intentions to help people made her “Grief” alive. As soon as it is alive, it could be defeated by a person. The poem is a chance for people to face with their own grief and try to find the solutions.

In general, Elizabeth Barrett Browning is the author of a number of interesting and educative poems. Those some of her works are full of complicated thoughts and word-combinations, the analysis helps to clarify the essence and use the lessons she wanted to share with the reader. Her “Grief” is her personal cry and her explanation of the situation she found herself after the death of her brother. The presence of simile, imagery, and metaphors, a properly chosen rhyme and meter, and educative diction promote the success of the poem and make people read and re-read it for several times in order to understand the message, find the answers, and get peace when passionless grief cannot be ignored, and the required portion of silence cannot be found out.

Appendix

Barrett Browning, Elizabeth. “Grief.” Poetry Out Loud, n.d., Web.

Grief

By Elizabeth Barrett Browning

I tell you, hopeless grief is passionless;

That only men incredulous of despair,

Half-taught in anguish, through the midnight air

Beat upward to God’s throne in loud access

Of shrieking and reproach. Full desertness,

In souls as countries, lieth silent-bare

Under the blanching, vertical eye-glare

Of the absolute heavens. Deep-hearted man, express

Grief for thy dead in silence like to death—

Most like a monumental statue set

In everlasting watch and moveless woe

Till itself crumble to the dust beneath.

Touch it; the marble eyelids are not wet:

If it could weep, it could arise and go.

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