Show that an interaction term of a dummy variable and a regressor changes the slope of a regression line..
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Show that an interaction term of a dummy variable and a regressor changes the slope of a regression line..
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- A negative correlation between variables X and Y will always result in a positive slope in the linear regression model. Cannot tell from the given information. False TruePlease rearrange the following steps of Multiple Linear Regression so that they are in order from the first step to the last step. (You may repeat this question if you don't get it all correct the first time.) Drag and drop options into correct order and submit. For keyboard navigation... SHOW MORE ✓ = Create scatterplots of each x-variable with the y-variable to assess the assumption linearity and confirm by checking correlations. III III = Review the Variance Inflation Factor values and use a backward-elimination process to remove the variable with the largest VIF above 10 and refit the model with the remaining x-variables and reassess the new VIF values. Use the backward-elimination procedure to remove any unneeded x-variables from the model to obtain the reduced model. III Perform a 6-step ANOVA test to determine if at least one x-variable in the full model is useful for predicting the y-variable. Check the residuals for the final model to determine if they are normally…The table below gives the number of hours spent unsupervised each day as well as the overall grade averages for five randomly selected middle school students. Using this data, consider the equation of the regression line, yˆ=b0+b1x, for predicting the overall grade average for a middle school student based on the number of hours spent unsupervised each day. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Hours Unsupervised 0 1 3 4 5 Overall Grades 95 92 85 81 62 Table Step 3 of 6 : Determine if the statement "Not all points predicted by the linear model fall on the same line" is true or false.
- The General Aviation Manufacturers Association has reported annual flying hours and fuel consumption for airplanes with a single, piston-driven engine as listedin file XR15057. Data are in millions of flying hours and millions of gallons of fuel, respectively. Determine the linear regression equation describing fuel consumption as a function of flying hours, then identify and interpret the slope, the coefficient of correlation, and the coefficient of determination. At the 0.05 level of significance, could the population slope and the population coefficient of correlation be zero? Determine the 95% confidence intervalfor the population slope.(you can see file XR15057 from photo)Why is it necessary that the variables are significantly correlated before performing regression analysis?When is the coefficient of certainty obtained from the regression equation equal to the square of the coefficient of correlation between the variables?
- A real estate agent wanted to find the relationship between sale price of houses and the size of the house. Shecollected data on two variables recorded in the following table for 15 houses in Seattle. The two variables are PRICE= Sale price of houses in thousands of dollarsSIZE= Area of the entire house in square feet. The Excel working has been given. Note: the left hand side is Regression run.. The right hand side is 'new' regression run (for ans d). Question a) Using MICROSOFT EXCEL- run the above regression and copy the output into your assignment word documentfrom which you can write down the least square regression line. Write down the least square regression linefrom that specific output. b) Interpret the slope and constant term with proper UNITS assigned. c) Comment on the explanatory power of the regression model from the required output. Copy that specificoutput into your assignment word document.Now to increase the explanatory power of the model the real estate agent decides…b) please3. In some data sets, there are values that are far from the linear regression line. What are the data points called?
- Discuss the effect on a regression analysis of not having data on one or more important predictor variables.A researcher collected statistics on the sales amount of a product in 120 different markets and the advertising budgets used in TV, radio and newspaper media channels for each of these markets. The sales amount are expressed in 1000 units, and the budgets are expressed in 1000$. The researcher wants to create a simple linear regression model by choosing one among the TV, radio and newspaper advertising budgets to explain the amount of sales. Accordingly, answer the following question by using the data in the "Regression Data Set" document in the appendix.1) b) In your opinion, which variable should this researcher choose as an independent variable to the simple regression model? Establish the simple linear regression model using the argument of your choice and write the equation for the model. Interpret b0 and b1.1) c) Test whether there is a statistically significant and linear relationship between the independent variable and the dependent variable by establishing the relevant…A county real estate appraiser wants to develop a statistical model to predict the appraised value of houses in a section of the county called East Meadow. One of the many variables thought to be an important predictor of appraised value is the number of rooms in the house. Consequently, the appraiser decided to fit the simple linear regression model, y = b₁x + bowhere y = appraised value of the house (in $thousands) and x = number of rooms. Using data collected for a sample of n=74 houses in East Meadow, the following results were obtained: y = 74.80 + 17.80x Give a practical interpretation of the estimate of the slope of the least squares line. For each additional room in the house, we estimate the appraised value to increase $74,800. For each additional dollar of appraised value, we estimate the number of rooms in the house to increase by 17.80 rooms. For a house with O rooms, we estimate the appraised value to be $74,800. For each additional room in the house, we estimate the…
