Question

A statistics teacher collected the following data to determine if the number of hours a student studied during the semester could be used to predict the final grade for the course. The default level of significance is .05 in excel. Use the default level of significance.

Student	Hours Studying	Final Grade
1	42	92
2	58	95
3	32	81
4	39	78
5	37	75
6	51	88
7	49	85
8	45	85

1. State your dependent and independent variable.

2. Find/State the coefficients for the intercept and slope.

bo = __________

b1= ____________

3. State the predicted linear regression model. Note: Adding a '^' is a bit cumbersome in Canvas. Please spell out 'predicted...'

4. State the correlation coefficient and interpret your result

5. State r-squared, also called the coefficient of determination, and interpret your result

6. Give an example of 1 independent variable that if added to the model could potentially lower/decrease r-squared. HINT: SSR + SSE = SST

Answer 1

DATH From the above Information dependent variabe Independnt AI QUAD CAMERA 0 REDMI NOTEIRO Grade final Hour studying variable bi= nexy- EASY nsx² - (EXIL 8* 30261 – 353 *879 8 x 1606g 13532 0.6089 boa ON De Ex 0.6089 x 353 679 8 8 bo 58.000 58.0061 + 0*6089 X correlation Coefficient (o) NEXY - Exey 10IX2-498). They lef2412 8*30261 353 *679 8* 16069 (-35877 /8 x 57953 167914 VIDHYRY

DATE for Test hois 48 to Test stanic ta r une Jiaga 0.76 * 18-2 I 1-10.7512 A = 2077 by bring pualue table for df = 6 prvaques 0.0324 0.05 P-value XL res fall do rgeet gue 820 There is relation between X&y coefficient of dermination IR. we no SST R² = SSR zlý y = 182.875 Sly-y) 322.875 ON bor se ob pr = 0.570002 pon 57 % variation in y in explained boy