Week-1 Week-2 Week- 3 Week-4 Actual scores: 65 64 130 30 Forecast scores: 105 65

Question

Week-1 Week-2 Week- 3               Week-4

Actual scores:                    65                          64                           130                         30

Forecast scores:               105                         65                           135                         65

·         Using the actual scores for Weeks 1, 2, 3, and 4 and your forecast values, calculate a regression equation, r, and R2 for this data.

·         Prepare a case study document to answer the following questions.

1. What data did you use in your regression equation? Which is the independent and dependent variable?

2. What is the regression equation, r, and R2 for your data?

3. Statistically, what does r and R2 tell you?

4. Based on your empirical evidence, how who well did you forecast your results? Justify your response.

You can use any method you like to complete this assignment; however, you need to show your work. Two items in Excel are useful. The correl function will return the correlation coefficient and add-in called "Data Analysis" has a function called regression that will provide all the rest of the information you should need.

Explanation / Answer

Question 1. What data did you use in your regression equation? Which is the independent and dependent variable?

Answer : We will use theh above given data in regressio equation where the independent variable is the actual value and predictred scores are dependent variable.

2. What is the regression equation, r, and R2 for your data?

I have use following exce function to calculate the above values.

THe intercept = INTERCEPT (y' values, x' values) =40.7673

Slope = SLOPE (y' values , x' values) = 0.716023

so regression line Forecasted scores (y) = 40.7673 + 0.716023 x (Actual score)

r = CORREL (y' values , x' values) = 0.8793

R2 = 0.87932  = 0.7732

(3) Here r tells us the strength, direction of linear reltionship between both variables. as here r > 0.8 and positive one that means relation between variables is positive one and the linear relation is strong.

R2 tells us the pecentage variation in dependent variable (forecstedl score in this case) is explained by independent variable (actual score).

(4) based on empirical evidence, we have forecsted aorund 77% well our forecasted results. as we established a linear relatiionship to get forecsted values bassed on actual results.

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