We conduct a simple regression of size on hhinc. The regression output is reportedin Table 1. Interpret the sign and magnitude of the estimated coefficients. The questions below are based on a dataset containing the characteristics of 5,407households. In particular, we will use the following variables:- size: home size, measured in square feet.- hhinc: annual household net income, measured in euros.- owner. dummy variable equal to 1 if the living space is owned, and 0 if the livingspace is rented.- hhsize1: dummy variable equal to 1 if the number of household's members is 1 or2, and 0 otherwise.hhsize2: dummy variable equal to 1 if the number of household's members is 3 or4, and 0 otherwise.hhsize3: dummy variable equal to 1 if the number of household's members is 5 orabove 5, and 0 otherwise.- edu: education level of household's members.The Appendix contains tables with the critical values of the standard normal distribution andthe F distribution. Table 1• regress size hhincSourcedfMSNumber of obs5,407F(1, 5405)1613.341 263141566Model263141566Prob > F0.0000ResidualR-squaredAdj R-squaredTotal1.1447e+095,406 211749.457Root MSEsizeCoefficientStd. err.tP>|t|[95% conf. interval]hhinc.0082545.000205540.170.000.0078516.0086574-cons800.98359.40491785.170.000782.5461819.4209

Consider the given table that shows the analysis of the Home size based on the annual household net…

Answered: We conduct a simple regression of size…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

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Problem 1P

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Can brand, battery life, and internal storage capacity affect a smartphone's price? Use MegaStat and α = .05 to perform a regression analysis for the Smartphones01BS dataset and answer the following questions. When you copy and paste output from MegaStat to answer a question, remember to choose to "Keep Formatting" to paste the text.
Can brand, battery life, and camera resolution affect a smartphone's price? Use α = .05 to perform a regression analysis for the Smartphones01BC dataset and answer the following questions. Solve this problem using MegaStat, and write your complete answers in the Response Box so I know that you can interpret the output. Attach your MegaStat file so I can see that you used the correct analysis method to solve this problem. Download: Smartphones01BC a. Perform the regression and write the complete regression equation. b. Based on your regression equation, which brand increases the price of a smartphone the most? Use the statistics from your output to explain your answer. c. Which predictors are significant, and which are not? Use the statistics from your output to explain your answer. d. Use your regression equation to predict the price of an LG smartphone with a 40-hour battery and 12-megapixel camera.
The difference between a regression weight and a beta weight is: A regression weight assumes linearity. A beta weight is for the population while a regression weight is for the sample. A regression weight is less biased. A beta weight is a standardized regression weight.
Listed below are systolic blood pressure measurements (in mm Hg) obtained from the same woman. Find the regression equation, letting the right arm blood pressure be the predictor (x) variable. Find the best predicted systolic blood pressure in the left arm given that the systolic blood pressure in the right arm is 85 mm Hg. Use a significance level of 0.05 Right Arm 101 100 94 75 Left Arm 174 167 146 144 Click the icon to view the critical values of the Pearson correlation coefficient r The regression equation is (Round to one decimal place as needed.) 76 144 CRITS
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Consider the accompanying data set of dependent and independent variables a. Perform a general stopwise regression using a 0.05 for the p-value to enter and to remove independent variables from the regression model b. Perform a residual analysis for the model developed in part a to verify that the regression conditions are met Click the icon to view the data a. Use technology to perform the general stepwise regression What is the resulting regression equation? Note that the coefficient is 0 for any variable that was removed or not significant -0.69 (050), (050)+(018) - X (Round to two decimal places as needed) • Data Table: y 63 43 51 49 40 42 23 37 30 27 20 31 FR 74 63 78 3534 52 44 47 35 17 15 20 17 Print X₂ 21 259. 15 9 38 18 17 5 40 27 30 33 x₂ 22 aadosa 2NNG 29 20 17 13 17 8 15 10 10 Done 1
We conduct a regression of size on hhinc, owner, hhsize1, hhsize2, and hhsize3. Wedo not include the constant. The regression output is reported in Table 3. Would youconclude that the home size increases with the household size? Interpret the signand magnitude of the estimated coefficients of hhsize1, hhsize2, and hhsize3
Listed below are systolic blood pressure measurements (in mm Hg) obtained from the same woman. Find the regression equation, letting the right arm blood pressure be the predictor (x) variable. Find the best predicted systolic blood pressure in the left arm given that the systolic blood pressure in the right arm is 90 mm Hg. Use a significance level of 0.05. Right Arm 101 100 92 75 75 O Left Arm 174 167 181 149 147 Click the icon to view the critical values of the Pearson correlation coefficient r The regression equation is y = + x. (Round to one decimal place as needed.) Given that the systolic blood pressure in the right arm is 90 mm Hg, the best predicted systolic blood pressure in the left arm is mm Hg. (Round to one decimal place as needed.)
Q1: [Regression and Correlation] The index of biotic integrity (IBI) is a measure of water quality in streams. As a data analyst you must monitor, track, and predict changes in water quality. You want to create a simple linear regression model that will allow you to predict changes in IBI in forested area. The following table conveys sample data from a coastal forest region and gives the data for IBI and forested area in square kilometers. Let forest area be the predictor variable (x) and IBI be the response variable (y). IBI X 47 72 21 72234 19 58 49 Forest Area Y 38 59 27 24 63 49 45 Required a. Q2: [Naïve Bayes] a. Write the pseudo-code of the following Naïve Bayes algorithm b. Consider the given dataset that classifies animals into two distinct classes. The classes are labeled as 'cat' and 'dog'. Use Naive Bayes classifier to figure out the class (cat/dog) of an instance if it has the following values of attributed Plot Scatter graph b. Determine the regression equation c. Compute…
Listed below are systolic blood pressure measurements (in mm Hg) obtained from the same woman. Find the regression equation, letting the right arm blood pressure be the predictor (x) variable. Find the best predicted systolic blood pressure in the left arm given that the systolic blood pressure in the right arm is 80 mm Hg. Use a significance level of 0.05. Right Arm 100 99 93 77 77 Q Left Arm 174 168 148 148 146 Click the icon to view the critical values of the Pearson correlation coefficient r The regression equation is ŷ=+x. (Round to one decimal place as needed.) mm Hg. Given that the systolic blood pressure in the right arm is 80 mm Hg, the best predicted systolic blood pressure in the left arm is (Round to one decimal place as needed.) Data table Critical Values of the Pearson Correlation Coefficient r α = 0.05 α = 0.01 0.950 0.990 0.959 0.878 0.811 0.917 0.754 0.875 0.707 0.834 0.666 0.798 0.632 0.765 0.602 0.735 0.576 0.708 0.553 0.684 0.532 0.661 0.514 0.641 0.497 0.623 0.482…
A researcher interested in explaining the level of foreign reserves for the country of Barbados estimated the following multiple regression model using yearly data spanning the period 2001 to 2016: FR=a+BOIL+YEXP+8FDI Where FR = yearly foreign reserves (S000's), OIL = annual oil prices, EXP = yearly total exports ($000's) and FDI = annual foreign direct investment ($000°s). The sample of data was processed using MINITAB and the following is an extract of the output obtained: Predictor Coef StDev t-ratio p-value Constant 5491.38 2508.81 2.1888 0.0491 OIL 85.39 18.46 4.626 0.0006 ЕXP -377.08 112.19 0.0057 FDI -396.99 160.66 -2.471 S = 2.45 R-sq = 96.3% R-sq (adj) = 95.3% Analysis of Variance Source DF MS F Regression 3 1991.31 663.77 ?? Error 12 77.4 6.45 Total 15 a) What is dependent and independent variables? b) Fully write out the regression equation c) Fill in the missing values **', **", '?'and ??"
Listed below are systolic blood pressure measurements (in mm Hg) obtained from the same woman. Find the regression equation, letting the right arm blood pressure be the predictor (x) variable. Find the best predicted systolic blood pressure in the left arm given that the systolic blood pressure in the right arm is 90 mm Hg. Use a significance level of 0.05. Right Arm 101 100 92 77 77 Left Arm 174 169 145 146 146

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We conduct a simple regression of size on hhinc. The regression output is reported in Table 1. Interpret the sign and magnitude of the estimated coefficients.