To solve this question, you need to perform simple linear regression, where you treat one variable as the explanatory variable and the other as the response variable. In this case, we will choose "attitude" as the explanatory variable and "job performance rate" as the response variable.
Step 1: Prepare the Data
Ensure that you have the data for employees' attitudes and their respective job performance rates. These would typically be in two lists, where each pair of corresponding values in the lists represents one employee.
Step 2: Compute Regression Line
Use the method of least squares to compute the slope (m) and y-intercept (b) of the regression line. The formula for the slope and y-intercept are given by:
`m = (N∑XY - ∑X∑Y) / (N∑X^2 - (∑X)^2)`
`b = (∑Y - m∑X) / N`
Where:
N is the number of observations
∑XY is the sum of the product of X and Y
∑X is the sum of X
∑Y is the sum of Y
∑X^2 is the sum of squares of X
Step 3: Round Off
Round off your slope and y-intercept to three decimal places.
Step 4: Form the Equation
Your linear regression equation will then be: y = mx + b, where y is the job performance rating, x is the attitude, and m and b are the computed slope and y-intercept, respectively.
Step 5: Test the Significance
To determine if attitude significantly affects job performance, you need to perform a hypothesis test for regression. For this, you normally use a significance level of 0.05.
If p-value from the test is less than 0.05, you reject the null hypothesis and conclude that "Attitude does affect job performance. The regression line is y = mx + b".
On the other hand, if p-value is not less than 0.05, you fail to reject the null hypothesis and conclude that "Attitude does not affect job performance significantly. The regression line is y = mx + b".
Always remember to analyze and interpret the results accordingly thereafter.