You work for a parts manufacturing company and are tasked with exploring the wear lifetime of a certain bearing. You gather data on oil viscosity used and load. You see the regression output given below. Predictor Coef Stdev t-ratio P
Constant -147.973 41.972 -3.53 0.004181
viscosity 6.262 0.474 13.21 <0.0001
load 0.298 0.04 7.43 <0.0001

s = 13.507 R² = 95.73% R² (adj = 95.02%

Analysis of Variance

Source DF SS MS F
Regression 2 49131.93 24565.96 134.65
Error 12 2189.38 182.45
Total 14 51321.3

Required:
What is the correct conclusion about the regression slopes based solely on the F-test

We reject the Null and conclude that There is significant evidence that the slope values are greater than 0.

Step-by-step explanation:

Based on the ANOVA output given :

The F critical value can be obtained thus ;

F(df regression, df error)

Using an α-value of 0.01

F(2, 12) at α = 0.01 is 6.927

The F statistic as obtained from the ANOVA table = 134.65

Since, F statistic > F critical we reject the Null and conclude that slope values are significantly > 0

Similarly,

Using the Pvalue :

The Pvalue of the slope are extremely small :

Viscosity <0.0001

Load <0.0001

At α = 0.01, 0.025

The Pvalue < α ; The null will be rejected.

3/4 ÷ 3/4

step-by-step explanation:

basically divide any proper fraction with itself to fit the criteria.

so if we are using 3/4, then:

3/4 ÷ 3/4 = 1

so 1 is the quotient.

and the numerators for both these fractions is 3.   and if we divide the numerators:

3 ÷ 3 = 1

we see that quotient is equal to our original quotient as well.

step-by-step explanation:

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