given the estimated simple linear regression equation

Does the equation that you developed in part (c) provide a good fit for the observed data? Explain. Let 33 = 0 if Bob Jones performed the service and 30g = 1 if Dave Newton performed the service (to 2 decimals). Develop the estimated simple linear regression equation to predict the repair time given the repairperson who performed the service. Regression is finding a relationship between a dependent and one or more independent variables using training dataset so that the approximate value of. Use the estimated regression equation to predict the selling price of a 2000 square foot house in Winston Salem, North. 05 level, is there a significant relationship between the two variables d. Recall that 22 0 if the type of repair is mechanical and 1 if the type of repair is electrical (to 2 decimals). Develop the estimated regression equation that could be used to predict the selling price given the number of square feet of living space. Develop the estimated simple linear regression equation to predict the repair time (y) given the type of repair (2 ). Ignore for now the months since the last maintenance service and the type of repair associated with the machine. Ignore for now the months since the last maintenance service (71 ) and the repairperson who performed the service. (to 4 decimals) because the p-value of 0.0020 shows that the relationship is not significant for any reasonable value 4.5 No of a. Regression analysis is commonly used for modeling the relationship between a single dependent variable Y and one or more predictors. Does the equation that you developed in part (a) provide a good fit for the observed data? Explain. The variable we are predicting is called the criterion. Recall that 32 = 0 if the type of repair is mechanical and 1 if the type of repair is electrical (to 2 decimals). In simple linear regression, we predict scores on one variable from the scores on a second variable. Develop the estimated simple linear regression equation to predict the repair time (Y) given the type of repair (X2). Ignore for now the months since the last maintenance service (21 ) and the repairperson who performed the service. DATA file 4.9 7 4.8 8 Repair Time Months Since in Hours Last Service Type of Repair Repairperson 2.9 2 Electrical Dave Newton 3.0 6 Mechanical Dave Newton 4.8 Electrical Bob Jones 1.8 3 Mechanical Dave Newton 2.9 2 Electrical Dave Newton Electrical Bob Jones 4.2 9 Mechanical Bob Jones Mechanical Bob Jones 4.4 4 Electrical Bob Jones 6 Electrical Dave Newton a. Click on the datafile logo to reference the data. Suppose that in addition to information on the number of months since the machine was serviced and whether a mechanical or an electrical repair was necessary, the managers obtained a list showing which repairperson performed the service. provides maintenance service for water-filtration systems.

For example, if you wanted to generate a line of best fit for the association between height and shoe size, allowing you to predict shoe size on the basis of a person's height, then height would be your independent variable and shoe size your dependent variable).Transcribed image text: Johnson Filtration, Inc. To begin, you need to add paired data into the two text boxes immediately below (either one value per line or as a comma delimited list), with your independent variable in the X Values box and your dependent variable in the Y Values box. Note: The first step in finding a linear regression equation is to determine if there is a relationship between the two. The results of the Analysis of Variance are given in Table 12 and the parameter estimates, estimated standard errors, t-statistics and p-values are given in. This calculator will determine the values of b and a for a set of data comprising two variables, and estimate the value of Y for any specified value of X. x and y are the variables for which we will make the regression line.

The line of best fit is described by the equation ŷ = bX + a, where b is the slope of the line and a is the intercept (i.e., the value of Y when X = 0). This simple linear regression calculator uses the least squares method to find the line of best fit for a set of paired data, allowing you to estimate the value of a dependent variable ( Y) from a given independent variable ( X). where is the y intercept (the value of Y where X 0), is the slope of the line, and is a random error term.

Given the estimated simple linear regression equation