The following table was generated from the sample data of 10 junior high students regarding the average number of hours they are unsupervised per night, the averagenumber of hours they play video games per night, and their final grades in their math class. The dependent variable is the final grade, the first independent variable (x1) isthe number of hours unsupervised each night, and the second independent variable (2) is the number of hours of video games each night.InterceptCoefficients Standard Error99.492882t-Stat6.492044Hours UnsupervisedHours Playing Video Games-1.878497-5.5966531.3680431.178139p-value15.325355 0.000005-1.373127 0.218817-4.750418 0.003157Step 1 of 2: Write the multiple regression equation for the computer output given. Round your answers to three decimal places.How to enter your answer (opens in new window)Answery=+x1 +x2Copy DataTablesKeypadKeyboard Shortcuts

Steps of solution From the given output table coefficientsintercept99.492882hours…

Answered: The following table was generated from…

ENGR.ECONOMIC ANALYSIS

14th Edition

ISBN:9780190931919

Author:NEWNAN

Publisher:NEWNAN

Chapter1: Making Economics Decisions

Section: Chapter Questions

Problem 1QTC

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Please show how to do b) and c). Quantity beef consumed is the dependent variable. Each of the others are the independent variables
(Econmetrics) Q.1 How can you test for general misspecification of model if it would have only (any of) two independent variables?
None
A researcher has a sample of 6 annual observations {94, 104, 102, 99, 111 and 107} for the CPI in country Z for the period 2015 to 2020, and wants to forecast CPI for the years 2021, 2022 and 2023. The researcher uses 3 different forecasting models: A, B and C. Model A is an AR(1) model with no drift and with an estimated autoregressive coefficient = 0.7. Model B is a MA(1) model with no constant and with an estimated MA coefficient = -0.4 (note the minus !). Model C is a random walk model with no drift. The error terms over the 2015-2020 period were estimated to have the values: {3, -1, 2, 4, -3, 1}. a. Compute the 2021, 2022 and 2023 forecasted values for the consumer price index based on the three models. Show the formulas and the details of your calculations, and explain all the related symbols. b. Suppose that the actual values of the CPI over the 2021, 2022 and 2023 were {108, 114, 105}. Calculate the Root mean square error of the three model forecasts over the 2021-2023…
6) Suppose you have the following data on the price of orange and the quantity sold: Price per Pound (in Quantity Sold (in Dollars) Pounds) 0.50 0.75 1.00 1.25 1.50 10 7 699 5 2 Assume that the quantity sold (Y) is a linear function of the price (X), i.e. Y₁ =B₁ + B₂X₁ + ε₁ Estimate the population regression coefficients. (Do not use Computer)
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The data for this question is given in the file 1.Q1.xlsx(see image) and it refers to data for some cities X1 = total overall reported crime rate per 1 million residents X3 = annual police funding in $/resident X7 = % of people 25 years+ with at least 4 years of college (a) Estimate a regression with X1 as the dependent variable and X3 and X7 as the independent variables. (b) Will additional education help to reduce total overall crime (lead to a statistically significant reduction in crime)? Please explain. (c) Will an increase in funding for the police departments help reduce total overall crime (lead to a statistically significant reduction in total overall crime)? Please explain. (d) If you were asked to recommend a policy to reduce crime, then, based only on the above regression results, would you choose to invest in education (local schools) or in additional funding for the police? Please explain.
For each of the following scenarios, indicate whether the relationship between the two variables is positive or negative, as well as which line on the previous graph has a slope that reflects this type of relationship. Hint: The X-axis and Y-axis on the graph are not labeled intentionally. You need to substitute the variables from each scenario for the horizontal and vertical axis. For example, in the first scenario, X-axis should be labeled “ ice-cream" and Y-axis should be labeled "The temperature". Relationship options: positive, negative Line options: A, B, C, D True or False: Line D has a slope of infinity.
In the linear model ,E (X*u) = a)X*u b) 0 c) u d) none of tha above
The following table was generated from the sample data of 10 college students regarding the number of parking tickets the student receives in a semester, the student's age, and the student's GPA. The dependent variable is the student's GPA, the first independent variable (x1) is the number of parking tickets, and the second independent variable (x2) is the student's age. Intercept Coefficients Standard Error 15.034003 1.302739 1-Stat p-value 11.540305 0.000025 Number of Parking Tickets -0.026275 0.030254 Student's Age -0.638331 0.062922 -0.868470 0.418515 -10.144838 0.000053 Copy Data Step 1 of 2: Write the multiple regression equation for the computer output given. Round your answers to three decimal places. Answer How to enter your answer (opens in new window) + x + x2 Tables Keypad Keyboard Shortcuts
You are given information on the use of public transportation in the data set within the image. Number of weekly riders y -variable Price per week (of using public transportation) x-variable Population of city x-variable Monthly income of riders x variable Average parking rates per month x-variable (a) Run a regression of the Number of weekly riders on Price per week, Population of city, Monthly income of riders, Average parking rates per month. Please copy and paste your regression results and do not submit your EXCEL worksheet. (b) If the average parking rates increase by $1, what will be the change in the number of weekly riders? Will this change be a statistically significant change? Please explain. (c) If the price per week (of using public transportation) increases by $1, what will be the change in the number of weekly riders? Will this change be…
Use a software of your own choice to calculate the daily log prices (lp) and daily log returns (Ir) i. Examine the descriptive statistics of both lp and Ir. What do you conclude about the distributions of lp and Ir? Is lp normally distributed? Is Ir normally distributed? Obtain the correlograms, and examine the autocorrelations and partial autocorrelations for both lp and Ir. What do you conclude about the behaviour of lp and Ir? Are they stationary/non-stationary? ii. iii. Are your conclusions about stationary/non-stationary of lp and lr confirmed by appropriate unit root tests?

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