You are given the following data, where X1 (final percentage in history class) and X2 (number of absences) are used to predict Y (standardized history test score in third grade): Y X1 X2
465 92 2
415 95 2
345 70 3
410 72 3
370 75 4
400 82 0
390 80 1
480 98 0
420 80 2
485 99 0
485 92 6
375 92 6
310 61 5
Determine the following multiple regression values.
Report intercept and slopes for regression equation accurate to 3 decimal places
Intercept: a =
Partial slope X1: b1 =
Partial slope X2: b2 =
Report sum of squares accurate to 3 decimal places:
SSreg = SS
Total =
Test the significance of the overall regression model (report F-ratio accurate to 3 decimal places and P-value accurate to 4 decimal places):
F-ratio =
P-value =
Report the variance of the residuals accurate to 3 decimal places.
Report the results for the hypothesis test for the significance of the partial slope for number of absences

Step-by-step explanation:

Hello!

Given the variables

Y: standardized history test score in third grade.

X₁: final percentage in history class.

X₂: number of absences per student.

Determine the following multiple regression values.

I've estimated the multiple regression equation using statistics software:

^Y= a + b₁X₁ + b₂X₂

a= 118.68

b₁= 3.61

b₂= -3.61

^Y= 118.68 + 3.61X₁ - 3.61X₂

ANOVA Regression model:

Sum of Square:

SS regression: 25653.86

SS Total: 36819.23

F-ratio: 11.49

p-value: 0.0026

Se²= MMError= 1116.54

Hypothesis for the number of absences:

H₀: β₂=0

H₁: β₂≠0

Assuming α:0.05

p-value: 0.4645

The p-value is greater than the significance level, the decision is to not reject the null hypothesis. Then at 5% significance level, there is no evidence to reject the null hypothesis. You can conclude that there is no modification of the test score every time the number of absences increases one unit.

I hope this helps!

step-by-step explanation:

answer:

sometimes

*i think