Consider a model where the true data are geneated as follows Y = 1 + 0.3*X + ɛ where X is uniform and ɛ is distributed as a standard normal random variable independently of X. Assume we are looking at a sample of size n=100 for which we fit a linear regression model and compute the training mean squared error. Carry out a Monte Carlo experiment with M-1000 replications in order to assess the variability and accuracy of the training mean squared error as a measure of fit. Give the standard error that results from your Monte Carlo experiment.
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- Fifty students take two midterm exams. On the first exam, the mean score is 65.0 and the standard deviation is 7.00. On the second exam, the mean score is 55.0 with a standard deviation of 10.00. The correlation coefficient of the two score is 0.70. Obtain the least squares regression line using the second exam score to predict the first exam score.Last school year, the student body of a local university consisted of 35% freshmen, 25% sophomores, 24% juniors, and 16% seniors. A sample of 300 students taken from this year's student body was taken to determine if there had been a significant change in % of students for each classification for this year as compared to last year. This analysis is an example of a Regression O Goodness of Fit Analysis of Variance Test of IndependenceWhat is the least squares regression line for the following scatter plot? y-hat = 0.89x + 1.36 y-hat = 0.31x + 1.95 y-hat = 0.67x + 0.87 y-hat = 1.05x + 1.37
- Aggregate data were collected for a recent year in 44 Denver neighborhoods on various demographic measures, including the crime rate (# of crimes per 1000 population). A multiple linear regression was used to build a predictive model predicting crime rate from four of these neighborhood characteristics: population size, % of children in the population, % of participation in free school lunch, and % change in household income over recent years. A portion of the ANOVA table from this model is below. Analysis of Variance Sum of DF Squares Mean Source Square F Value Pr>F Model 299771 74943 <.0001 Error Corrected Total 43 473806 Provide the 5 values that have been omitted from this table. Specifically, provide: df(model), df(error), SS(error), MS(error), and the F statistic. Include intermediate calculations wherever used.Consider a completely randomized design involving four treatments: A, B, C, and D.Write a multiple regression equation that can be used to analyze these data. Define allvariablesThe data from a random sample of 75 students was used to fit a least squares regression line relating test scores to the number of hours spent playing Left for Dead 2 each week. The explanatory variable (hours spent playing Left for Dead 2) had a range of 0- 18 and the response variable (test score) ranged from 55 - 99%. The resulting prediction equation is y = 99 - 4.3x. Furthermore, the correlation coefficient is 0.85. Determine if the following statements are TRUE or FALSE. a. The association between hours playing Left for Dead 2 and test score is strong. b. As hours spent playing Left for Dead 2 decreases, test score decreases.
- A company that manufactures computer chips wants to use a multiple regression model to study the effect that several variables x, x,, have on the total daily production cost (in 1000's of dollars). If a regression model is estimated using 100 observations on these variables, fill in the blanks in the following analysis of variance (ANOVA) table that is associated with this model. Do all calculations to at least three decimal places. Degrees of freedom Source of Sums of Mean F statistic variation squares squares Regression 7 246 Error 92 49 Total 99 295lecturer would like to know if the foundation Cumulative Grade Point Average (CGPA) predicts the undergraduate CGPA of engineering students at year one. She obtained the relevant information from 105 randomly selected students as follows: Σx= 323.02 , Σy= 33.15, Σxy 1043.626, > x? = 1021.487, ) y² = 1077.836 (i) Obtain the least square regression line, ŷ = a + bx for the study. (ii) Estimate the strength of the association between the foundation CGPA and CGPA in year one. Interpret the result.Consider a model where the true data are geneated as follows Y = 1 + 0.3*X + ε where X is uniform and ε is distributed as a standard normal random variable independently of X. Assume we are looking at a sample of size n=100 for which we fit a linear regression model and compute the training mean squared error. Carry out a Monte Carlo experiment with M=1000 replications in order to assess the variability and accuracy of the training mean squared error as a measure of fit. Give the standard error that results from your Monte Carlo experiment.
- The price paid for a new phone of a particular model follows a Normal distribution with mean μ=750 and standard deviation σ= 42. What proportion of these phones are purchased for more than 821 dollars? Give your answer to four decimal places. A study of 25 online jewelry retailers was done to find the statistical relationship between the price yyin dollars of a diamond ring and the weight xxin carats of the diamond. Based on this data the following least-squares regression line was found: y^=−6047.75+11975.14x What is the predicted price of diamond ring a 2.2 carat diamond using this regression line. Round your answer to two decimal places. A study of 25 online jewelry retailers was done to find the statistical relationship between the price yy in dollars of a diamond ring and the weight xx in carats of the diamond. Based on this data the following least-squares regression line was found: y^=−6047.75+11975.14x One online retailer sells a 3.00 carat diamond ring for 28,999…The manufacturer selects 3 of the candidate independent variables to use in a multiple regression model for estimating y, the amount of scrap cloth (in square feet). Using data collected from 31 different cutting machines operating on different days, the model y = Bo+B1x,+B,x,+Bzxz is fit to the data. Fill in the blanks in the analysis of variance (ANOVA) table associated with this model. Do all calculations to at least three decimal places. Degrees of freedom Sums of Source of variation Mean F statistic squares squares Regression Error Total 538Compute the least-squares regression line for the given data set.