Question 5 You have a sample of 223 observations about the daily returns of a company called VA and of an index fund called IND. The table below provides some partial results about a simple linear regression in which VA is the dependent variable and IND is the independent variable. The fitted regression model is: VA = bo + bị* IND. Explain and %3D justify clearly your answer to each question. Give the numerical value when possible. Coefficient Estimate Standard Test Statistic Upper 95% Error Lower 95% Intercept 0.0000584 Q1 0.052 -0.0021549 93 Slope 오2 0.0555300 30.581 오4 1) What is the value that should appear in the cell with the label Q1? 2) What is the value that should appear in the cell with the label Q2? 3) What is the value that should appear in the cell with the label Q3? 4) What is the value that should appear in the cell with the label Q4?

Question 5 You have a sample of 223 observations about the daily returns of a company called VA and of an index fund called IND. The table below provides some partial results about a simple linear regression in which VA is the dependent variable and IND is the independent variable. The fitted regression model is: VA = bo + bị* IND. Explain and %3D justify clearly your answer to each question. Give the numerical value when possible. Coefficient Estimate Standard Test Statistic Upper 95% Error Lower 95% Intercept 0.0000584 Q1 0.052 -0.0021549 93 Slope 오2 0.0555300 30.581 오4 1) What is the value that should appear in the cell with the label Q1? 2) What is the value that should appear in the cell with the label Q2? 3) What is the value that should appear in the cell with the label Q3? 4) What is the value that should appear in the cell with the label Q4?

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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Study 3: Age & Pain. Using the same data set PillShapeColor - use Pearson Correlation and simple linear Regression to create results and then summarize these in APA-style. Run a Pearson Correlation on age and pain rating. Create a scatterplot with pain rating on Y and age on X axis. Run a simple linear regression model with pain rating as DV and age as IV. Regression → Linear Regression, choose your DV, IV(s) –use defaults settings. 1-What is the equation for the regression line? How much variance in pain is accounted for by age (R2)? Interpret this in context of the study (is this a lot of variance explained or not much).2-Describe the regression results simply and clearly- include the slope and constant and describe what the slope means. Is age a significant predictor of perceived pain? Finally, describe what the model predicts about the pain rating of a person who is 40 years old and someone 70 years old.
please do all parts! The estimated regression equation for this data set is y=4.4878+1.9549x. Part A: (in image) Part B: is the linear function is the appropriate regression function for this data set? Part C: do the residuals have a constant variance? Part D: are the residuals independent? Part E: are the error terms are normally distributed? y x22 821 818 846 2241 2254 2276 3258 3268 32
A researcher wants to predict the effect of the number of times a person eats every day and the number of times they exercise on BMI. What statistical test would work best? A. Pearson's R B. Spearman Rho C. Linear regression D. Multiple regression
Arestricted model in the regression analysis is also called: a. a reduced model b. an unrestricted model c. a complete model d. a one-way ANOVA model
Please see attached. Is there a way to interpret using excel?
Which of the following situations would have data sets or plots that could have a regression line with a positive slope? Select all that apply. Select all that apply: The cumulative number of ships launched by ship builders as a function of the number of months since the start of the year The number of cars produced as a function of the number of days since the end of the last strike. The total shipping orders that are backlogged as a function of the number of ships in drydock. The number of backorders as a function of the number of assembly workers in a manufacturing company that makes the product.
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: ??=?+????+????+???I Where FR = yearly foreign reserves ($000’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 EXP -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 SS MS F P Regression 3 1991.31 663.77 ? ?? Error 12 77.4 6.45 Total 15 e) Perform the F Test making sure to state…
The least-squares regression equation is y=620.6x+16,624 where y is the median income and x is the percentage of 25 years and older with at least a bachelor's degree in the region. The scatter diagram indicates a linear relation between the two variables with a correlation coefficient of 0.7004. Interpret the slope.
As a marketing manager for TriFood, you want to determine whether store Sales (# sold in one month) of TriPower bars are related to price (in cents) of TriPower bars and in-store promotional expenditures (in dollars) for TriPower bars. You conduct a multiple regression analysis with store Sales (Y) as the response variable, and Price (X1) and Promotion (X2) as explanatory variables. Use the pictured Excel regression output below to answer the questions. a) Interpret the value for R square. Interpret the estimated coefficient for price. b) State the hypotheses for assessing the statistical significance of the overall regression equation. Does the model overall fit the data (yes or no?) f) An external consultant to TriFoods believes that for every $1 increase in promotional expenditures, sales will increase by 4.7 units. Test the consultant's hypothesis at a 5% significance level using both approaches (tcalc vs tcrit and p-value vs a).
A sample consists of 500 houses sold in Karachi between January 2020 and December 2020. The multiple linear regression analysis is carried out to predict the house prices for investment in residential properties in Karachi, Pakistan. The output below is produced using SPSS. (300 words) Table: Coefficients Model Unstandardized Coefficients t VIF Constant 14.208 5.736 Age of house -0.299 -2.322 1.58 Square footage of the house 0.364 2.931 1.71 Income of families in the area 0.004 0.392 1.01 Transportation time to major markets -0.337 -2.619 1.90 R² = 0.67; DW = 2.08 Dependent Variable: House price (Pakistani rupees in Million) a) You are required to write the multiple regression equation. b) How would you interpret the above Output’ of a regression analysis performed in SPSS? c) From the above results, what can you say about the nature of autocorrelation? d) Is there multicollinearity in regression? How do you know?
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