SUMMARY OUTPUT Regression Statistics Multiple R 0.769489638 R Square 0.592114303
ID: 3219918 • Letter: S
Question
SUMMARY OUTPUT Regression Statistics Multiple R 0.769489638 R Square 0.592114303 Adjusted R Square 0.56073 Standard Emor 793089 Obsenations ANOVA ance F Regression 1 1.18701E 13 1.19E 13 18.87167 0.000795544 13 8.17687E 12 6.29E+11 20047E+13 Standard Error intercept t Stat 4833.942857 4309313949 0112 0.9122 426136 7357 935804.6214 -926136.7357 935804 205896, 1071 47396.13437 4.344154 0.000796 103502.964 308289 2303 103502984 308289 X Variable 1 1. (2 Points) describe the two purposes of using a regression analysis in the business decisions. predict on 2. (2 points) what can you measure if you use a regression analysis? a. Pattern b. Trend c. Seasonality d. Irregularity 3. (2 points) take a look at number 1. Interpret 0.5607 (Do not simply say it is a measure of how close a the data are to a regression line, and focus on the number) 4. (2 points) take a look at number 2. What does 15 mean in the table?Explanation / Answer
Since I cannot see the table I am going to write X and Y instead or e.g. height and weight.
As per guideline we are supposed to answer only 4 parts however since these are easy one I will answer all 5.
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5. X variable measure the cause and Y variable measures the effect.
6. The slope/coefficient estimate equals the average change in Y associated with a unit change in X. Here a unit change in the value of X with bring about 205896 units change in the value of Y
7. P-value is very low here, lower than 0.05 hence we can reject the null hypothesis.
8. 95% is the margin for error in terms of uncertainty. Here it means if you run similar test of another set from the sample you will get the same results 95% of times.
9. Formula is y = mx + b, here m is the slope, and b is where y-intercepts.
10. The formula is important because it lets the experimenter predict the value of y variable based on the change in the x variable. Regression tells you how strong or weak is the relationship between two variables. It also tells you how many units one will change is other is changed by 1 unit. In the practical world, this would tell decision makers what effect or the extent of effect their decision will have on e.g. cost or customer experience etc.
Hope this helps
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