A linear regression model is fitted to determine if a relationship exists between consumer rating of cereal(response)and the number of grams of sugar contained in each serving, grams of fat per serving and grams of dietary fibre perserving as predictors. The sample used was of size 77.(a) The regression sum of squares of fitted model was 13002.226 and error sum of squares was 1994.574. Test forsignificance of fit. (b) Part of the results of the model fit were as follows:fat(1) Compute the test statistic for testing for the significance of the following predictor(round off answer to onedecimal point):sugarfibreVariable B s.e(B)Intercept 53.437Based on part (i);sugar-1.96 0.142fat-3.480.621fibre 2.95 0.255each of the three predictors is significantly related to response after adjusting for other two predictorsnone of the three predictors is significantly related to response after adjusting for other two predictorsfibre is the most significant predictor after adjusting for other two predictorsnone of the provided answers is correct4

Given, regression sum of square (SSR) = 13002.226, error sum of squares (SSE) = 1994.574, sample…

Answered: (b) Part of the results of the model…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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The t- statistic is
A model was built to predict house prices that included 8 variables. The Overall F test had a p-value equal to 0.0001, but all of the p-values for the t-test had p-values greater than 0.20. What does this indicate? The data is summarized. O There is a problem with extrapolation. O There is a problem with multicollinearity.
The Cadet is a popular model of sport utility vehicle, known for its relatively high resale value. The bivariate data given below were taken from a sample of fifteen Cadets, each bought new two years ago, and each sold used within the past month. For each Cadet in the sample, we have listed both the mileage x (in thousands of miles) that the Cadet had on its odometer at the time it was sold used and the price y (in thousands of dollars) at which the Cadet was sold used. The least-squares regression line for these data has equation y = 40.63 - 0.46x . This line is shown in the scatter plot below. Mileage, x(in thousands) Used selling price, y(in thousands of dollars) 23.9 29.5 37.6 22.6 20.8 30.9 23.3 33.1 28.3 26.1 27.3 29.9 27.7 29.8 20.9 30.3 25.8 27.2 34.4 25.9 23.9 27.4 23.8 27.5 23.2 31.4 15.6 34.0 29.4 27.7 Send…
Various doses of poisonous substance were given to groups of 25 mice and the following results were obtained in Table Q1: Table Q1 Dose (mg) Number of deaths 4 1 6. 3 6. 10 8 12 14 14 16 16 20 (b) Calculate the sample correlation coefficient between the dose and the number of death. Interpret the result. (c) Calculate the value of Sum Squares Error (SSE) and Mean Square Error (MSE). (d) At the 0.05 level of significance, test the slope greater than one.
18.8 Drove when drinking alcohol. Table 18.7 presented cross-tabulated results for the number of adolescents who drove or rode in a car or other vehicle when the driver was drinking alcohol. Prevalence proportions varied from 11.3% (non-Hispanic white) to 4.9% (non-Hispanic black). Test the data in Table 18.7 for significance by calculating its chi-square statistic, df, and P-value.
The data is presented in parallel format. Before running the ANOVA in Excel, you will need to put it into serial (column) format. You will need to create a categorical variable for the first factor (days) and for the second factor (time of day). This is a critical step. The data presented shows exam scores for students randomly assigned to the sections meeting on these days at these times of the day: Thursday Friday Morning Afternoon Morning Afternoon 59.1 60.6 50.2 34.7 50.6 60.5 39 44 57.1 59 71.2 43.7 58.6 45.7 57.5 52.9 51.1 72.6 50 53.3 40.1 39.8 58.1 34.6 27.3 59.3 38.9 53.2 27.3 72.9 58.2 52.9 39.5 45.9 57.9 46.8 56.6 40 71 46.7 Conduct a 2-way ANOVA. First, find the group means for each level of the factors. (Report answers accurate to 2 decimal places.) Days: MThursday MFriday Time of day: Mmorning Mafternoon Report the results of the ANOVA for the main & interaction effects. (Report P-values accurate to 4 decimal places and F-ratios accurate to 3 decimal places.) Day: FA = p…
2. Five 2 cm thick experimental pieces of an insulation material were tested atrandom. Resulted compression (y) was measured recorded for the given amount ofmeasured pressure. The data are listed below:Pressure Compression1 12 23 24 35 5Fit a simple linear model relating compression y to the applied pressure x and find:a) error variance.b) the coefficient of correlation.
The weights (in pounds) of 6 vehicles and the variability of their braking distances (in feet) when stopping on a dry surface are shown in the table. Can you conclude that there is a significant linear correlation between vehicle weight and variability in braking distance on a dry surface? Use α=0.01. Weight, x 5920 5370 6500 5100 5890 4800 Variability in 1.76 1.91 1.50 braking distance, y EEE Click here to view a table of critical values for Student's t-distribution. Setup the hypothesis for the test. Ho: P Ha: P 0 0 1.86 1.62 1.67 M
1. Was a dependent or independent t-test used to analyze the data? How do you know?
Managers of an outdoor coffee stand in Coast City are examing the relationship between (hot) coffee sales and daily temperature, hoping to be able to predict a day's total coffee sales from the maximum temperature that day. The bivariate data values for the coffee sales (denoted by y, in dollars) and the maximum temperature (denoted by x, in degrees Fahrenheit) for each of fifteen randomly selected days during the past year are given below. These data are plotted in the scatter plot in Figure 1. Also given are the products of the temperature values and coffee sales values for each of the fifteen days. (These products, written in the column labelled "xy," may aid in calculations.)
3. Using the attached data set (or CPS08 from the textbook Empirical Exercise E4.1), run a regression of average hourly earnings (AHE) on Age and carry out the following exercise: a. Is the estimated regression slope coefficient statistically significant (or, can you reject the null hypothesis Ho: B₁ = 0 versus a two-sided alternative at the 5% significance level? What is the p-value associated with coefficient's t-statistics? b. Construct a 95% confidence interval for the slope coefficient. SUMMARY OUTPUT Regression Statistics Multiple R 0.17036366 R Square 0.02902378 Adjusted R Sq 0.02889782 Standard Erro 9.99186151 Observations 7711 ANOVA Regression Residual Total df SS MS F Significance F 1 23005.7375 23005.7375 230.432297 2.6346E-51 7709 769645.718 99.8372964 7710 792651.456 Lower 95.0% Upper 95.0% Coefficients Standard Error t Stat 1.08227522 1.18425524 0.91388679 P-value Lower 95% Upper 95% 0.36080492 -1.2391869 3.40373732 -1.2391869 3.40373732 Intercept X Variable 1 0.60498629…
The Cadet is a popular model of sport utility vehicle, known for its relatively high resale value. The bivariate data given below were taken from a sample of fifteen Cadets, each bought new two years ago, and each sold used within the past month. For each Cadet in the sample, we have listed both the mileage x (in thousands of miles) that the Cadet had on its odometer at the time it was sold used and the price y (in thousands of dollars) at which the Cadet was sold used. The least-squares regression line for these data has equation y = 40.86-0.45x. This line is shown in the scatter plot below. Mileage, x (in thousands) 29.7 27.8 26.8 24.2 21.1 23.0 24.3 15.4 37.7 23.6 34.4 27.8 23.5 20.9 259 Used selling price, y (in thousands of dollars) 27.4 29.4 31.2 30.1 31.7 31.7 27.2 34.2 23.4 28.2 26.3 26.5 33.7 30.6 267 Used selling price (in thousands of dollars) 40- 35+ 30- 25- 20 THIS 15 X 20 X X X 30 Mileagex (in thousands) X 35 40

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