The data below are the temperatures on randomly chosen days during the summer and the number of employee absences at a local company on those days. Test the claim, at the α = 0.05 level of significance, that a linear relation exists between the two variables, Apply classical and traditional approaches. Show step1-step3. Steps for Hypothesis testing.Step1)State null and Alternative Hypothesis.Step2) Apply p-value and Classical(Traditional) ApproachClassical Approach: CV vs Test Statisticp-value Approach: p-value vs Alpha (Level of Significance)Step3)State conclusion.Claim is Alternative Hypothesis:Reject Null Hypothesis: There is sufficient evidence to conclude thatDo not reject Null Hypothesis: There is insufficient evidence to conclude thatClaim is Null Hypothesis:Reject Null Hypothesis: There is sufficient evidence to reject thatDo not reject Null Hypothesis: There is insufficient evidence to reject that The data below are the temperatures on randomly chosen days during the summer and thenumber of employee absences at a local company on those days. Test the claim, at the a = 0.05level of significance, that a linear relation exists between the two variables, Apply classical andtraditional approaches. Show step1-step3.Input B1 as "beta1" or "B1". Do not input as "b1".Temperature x7285919410080907899number of absences y371010841515

The data inserted in Excel is defined as: In this case, 'Temperature' is the independent variable(X) and 'Number of Absences' is the dependent variable(Y). Procedure to obtain least square regression line using EXCEL is as follows: Enter the data values in the excel sheet. In Data, select Data Analysis and choose Regression. Select 'B1:B10' for Input Y Range. Select 'A1:A10' for Input X Range. Select Labels. Click OK.Output obtained using Excel is : Thus, the regression line can be written as: