APznzaZ0VYbZToW2gJ-V8bmHJTZ3jOJqHwgRzOjk1O2FUCgDJxupzMKEUUFPCbK1506v-33GwqVPM7J1RQZ23rg9QiU0UV13JpUZ

The following graph shows the relationship between real GDP growth and change in unemployment for the US between 1961 and 2013. US (1961-2013) Change in unemployment rate (%) 6 4 6 2009 -1 0 y=-0.3768x+1.2298 R=0.641 4 Real GDP growth (%) The equation shown is the regression result for the best-fitting line. Based on this information, which of the following statements is correct? 9 a) With real GDP falling by 2.8% in 2009, the predicted rise in the unemployment rate would have been 2.3% b) From the regression result, policy makers can be sure that a 1 percentage point increase in real GDP next year will definitely lead to a fall in the unemployment rate of 0.38%. Oc) The unemployment rate remains stable when there is zero real GDP growth. d) Okun's coefficient for the US is 1.2298

Run the Multiple Linear Regression using Fatalities as the dependentvariable and Licensed drivers, registered vehicles, and GDP per capita as independentvariables.  Submit Excel FileCalculate the following:a- Interpret the R-squareb-Find the equation of the fitted linec- Which variable has the strongest relationship with number of fatalities?d- Identify which variables are significant/non-significant using alpha = 0.05?   Fatalities  Licensed Drivers Registered Vehicles GDP per Capita (measured) 953 3999057 5300199 40598 80 536033 803684 71996 1010 5284970 5806313 43464 516 2145334 2817145 38919 3563 27039400 31022328 68970 632 4244713 5356018 59885 294 2605612 2879802 68555 111 786504 1008468  64895 31 527731 351933 176498 3133 15368695  17496002 43423 1504 7168733 8512550 50288 117 948417 1267385 58185 231 1252535 1879670 40189 1031 8714788 10588910 60419 858 4589405 6190736 49209 318 2260271 3691892 54520 404 2149430 2684010 53094 724…

Q3. Let the given data set be as follows Time GDP (In crore) 2020 2 2021 5 2022 Based on the above information, answer the following questions. i. ii. Fit the regression model by using the matrix method as GDP, a+pGDP +& Find the estimated error term. Forecast the GDP for the year 2023. ソーダ 12

A company sets different prices for a particular DVD system in eight different regions of the country. The accompanying table shows the numbers of units sold and the corresponding prices (in dollars). Sales 420 380 350 400 440 380 450 420 Price 104 195 148 204 96 256 141 109a. Graph these data, and estimate the linear regression of sales on price. b. What effect would you expect a $50 increase in price to have on sales?

The following data relate the sales figures of the bar in Mark Kaltenbach's small bed-and-breakfast inn in portland, to the number of guest registered that week:                            week            guests         bar  sales                                 1                 16              $330                                 2                  12              $270                                 3                  18              $380                                4                   14              $315 a) The simple linear regression equation that relates bar sales to number of guests(not to time) is (round your responses to one decimal place):                           Bar sales = [___]+[___]X guests

The numbers of polio cases in the world are shown in the table for various years. Year Number of Polio Cases (thousands) 1988 1992 1996 2000 2005 2007 Let f(t) be the number of polio cases in the world t years since 1980. Use a graphing calculator to draw a scattergram of the data. Is it better to model the data by using a linear or exponential model? Select an answer Find an equation of f. Hint f(t) = 350 138 33 4 3.2 1.3 The number of polio cases Select an answer Hint Round the coefficients to 2 digits. Predict the number of polio cases in 2017. years by Select an answer Predict in which year there will be 1 case of polio. Find the approximate half-life of the number of polio cases. Hint per year.

GSU is trying to predict how price of books predict the quantity of books sold over the semesters. Perform a liner regression analysis, and and find the best model to predict quantity of books to stock in the book store. Make recommendation at setting the prices and quantity at their optimum values for maximizing quantity of books sold. Be prepared to discuss your analysis. Quantity Price 180 475 590 400 430 450 250 550 275 575 720 375 660 375 490 450 700 400 210 500

The table shows the yield (in bushels per acre) and the total production (in millions of bushels) for corn in a country for selected years since 1950. Let x represent years since 1900. Find a logarithmic regression model (y = a + b In x) for the yield. Estimate the yield in 2029. The regression model is y=+()Inx. (Round to one decimal place as needed.) ~ Year 1950 1960 1970 1980 1990 2000 2010 X Yield 50 60 70 80 90 100 110 41 54 85 96 110 146 158

Suppose you want to study the relationship between city GDP and how many companies are operated by government using a single regression 〖ln⁡(GDP)〗_i =8.5+ 0.013〖govc〗_i  + u_i.  Govc means the number of companies operated by government in the city. Before you do anything, interpret the slope coefficient. If one city has 5 government operated companies, predict the GDP. (answer the GDP that is not logged) Now assume the standard error of β_0 (intercept) is 2, standard error of β_1  (slope) is 0.01, and the sample size is n=200, write down and explain the steps of conducting significance test by hand for the estimated coefficient on 〖govc〗_i  in detail. What does the test result imply? If the sample size is n=25, a very small sample size, standard error of β_1  (slope) is still 0.01, will your steps change?    Suppose your fail to reject the hypothesis in question 3). Apply the null hypothesis i.e. the parameter equals to the number in your null, show that R^2=0 in this case. What does…

The diagram shows what happened to the consumption of lamb in the UK over the period 1974– 2015. How can we explain this dramatic fall in consumption? One way of exploring this issue is to make use of a regression model, which should help us to see which variables are relevant and how they are likely to affect 140 130 120 110 100 90 80 70 60 50 40 30 20 1974       1978      1982       1986       1990      1994       1998       2002       2006      2010       2014   Note: Data from 2015 based on end of financial year. Source: Based on data in Family Food datasets   UK consumption of lamb: 1974–2017   The following is an initial model fitted to annual data for the years 1974–2010. QL = 144.0 – 0.137PL – 0.034PB + 0.214PP  – 0.00513Y + e                                                                           (1) where: QL is the quantity of lamb sold in grams per person per week; PL is the ‘real’ price of lamb (in pence per kg, 2000 prices); PB is the ‘real’ price of beef (in pence per…

The table below shows the number, in thousands, of vehicles parked in the central business district of a certain city on a typical Friday as a function of the hour of the day. Hour of the day Vehicles parked(thousands) 9 A.M. 6.2 11 A.M. 7.4 1 P.M. 7.5 3 P.M. 6.6 5 P.M. 3.9 (a) Use regression to find a quadratic model for the data. (Let V be the number of vehicles and t be the time in hours since midnight. Round the regression parameters to three decimal places.) V =        (b) Express using functional notation the number of vehicles parked on a typical Friday at 4 P.M., and then estimate that value. (Round your answer to two decimal places.) V    =     =   thousand

4. The following regression is fitted using variables identified that could be related to tuition charges ($) of a university. TUITION = a+ B ACCEPT + y MSAT + 1 VSAT Where ACCEPT = the percentage of applicants that was accepted by the university, MSAT = Median Math SAT score for the freshman class and VSAT = Median English SAT score for the freshman class. The data was processed using MNITAB and the following is an extract of the output obtained: Predictor Coef StDev Constant -26780 6115 ACCEPT 116.00 37.17 MSAT -4.21 14.12 VSAT 70.85 15.77 т P -4.38 0.000 0.003 -0.30 4.49 0.767 ** S = 2685 R-Sq 69.6% R-Sq (adj) = 67.7% Analysis of Variance Source DF SS MS Regression 3 Residual Error 49 Total 52 808139371 353193051 1161332421 269379790 7208021 F 37.37 Р 0.000 a) Write out the regression equation. b) State the dependent and independent variable(s) c) Fill in the blanks identified by ** and ****. d) Is significant, at the 10% level of significance? [1] [2] [6] [4] e) State one…

3. Finally, think about the following applied situation: An economist wants to estimate a line that relates personal consumption C and disposable income I. The economist interviews a few households and obtains the following data: Income Consumption (thousands of dollars) (thousands of dollars) 20 18 18 13 27 21 36 27 37 26 45 36 50 39 a) With the help of your graphing calculator, make a scatter plot and decide whether a linear model is appropriate. Carefully draw the scatter plot on your papers, as well.

Conduct a simple regression analysis

Stores commonly offer a cheaper unit price for large quantity purchases. Quantity 1 2 5 10 20 Unit Price $100.00 $80.00 $70.00 $50.00 $40.00 a. Use regression to find a logarithmic equation to model the data. Round the numbers in your equation to 2 decimal places. y = a + bln(z) with You b b. Use your equation to find an appropriate unit price for a customer who purchases 15 items. c. Use your equation to find an appropriate unit price for a customer who purchases 25 items. $

The Ipod Touch has been out for several years now and a lot of data has been collected. There is a functional relationship between the Price of an IPod Touch and Weekly Demand. Below is a table of data that have been collected Price P ($) Weekly Demand S (1,000s) 150 207 170 208 190 192 210 190 230 185 250 169 A.. Find the linear model that best fits this data using regression and enter the model below (for entry round the slope value to nearest 0.01 and constant parameter to nearest 1) T(p) = Now answer these two questions: B.. What does the model predict will be the weekly demand if the price of an ipod touch is $191 ? (nearest 100) C.. According to the model at what should the price be set in order to have a weekly demand of 194,800 ipod Touches? $ (nearest $1) Note: In the "real" world Apple sold about 20 million Ipod Touch's from Sept. 2007-Sept. 2009

What is a linear regression model? What is measured by the coefficients ofa linear regression model? What is the ordinary least squares estimator?

2. Question 2: Suppose that you estimate a model of the aggregate annual retail sales of new cars that specifies that sales of new cars are a function of real disposable income, the average retail price of a car adjusted by the consumer price index, and the number of sports utility vehicles sold (you decide to add this independent variable to take account of the fact that some potential new car buyers purchase sports utility vehicles instead). You use the data (annual from 2000 to 2014) and obtain the following estimated regression equation: CARS, = 1.32 + 4.91Y D; + 0.0012 PRICE, - 7.14 SUV (2.39) (0.00045) (71.40) 1 where CARS = new car sales (in hundreds of thousands of units) in year t, YD; = real disposable income (in hundreds of billions of dollars), PRICE = the average real price of a new car in yeart (in dollars), SUV = the number of sports utility vehicles sold in year t (in millions). You expect the variable YD to have a positive coefficient and the variables PRICE and SUV to…

Refer to the quarterly value of Gross Domestic Product (GDP in billions of current dollars) in China from 2021q1 to 2023q4. You are going to analyze GDP by an additive model with a trend component (Tt) and a seasonal component (St), and forecast GDP using the seasonal decomposition method. Year Quarter t GDP (Y+) CM(4) Detrended 2021 1 1 24920 2 2 28284.9 3 3 29128.8 ? ? 4 4 32589.9 ? ? 2022 1 5 27034.4 ? ? 2 6 29244.7 ? ? 3 7 30794.2 ? ? 4 8 33399.1 ? ? 2023 1 9 28442.3 ? ? 2 10 30829.3 ? ? 3 11 31997.6 4 12 34789 a. Use an CM(t|4) [centered moving average of order 4] series to estimate the GDP series from t=3 to 10. b. Calculate the seasonal factors for each quarter with a zero mean, by first obtaining the detrended revenue series from t = 3 to 10. c. In this seasonal decomposition method, why is it necessary to use moving averages to process time series data first? How about using moving average of order 5?

if we got the the following regression result Yi= -2.5-10 (Xi) when the number of observation =100. then as Xi increase, Yi will and when Xi increases by 10, Yi will........ by.........*

3. The following model is a simplified version of the multiple regression model used by BEST Econometrics Group to study the trade off between time spent sleeping and working to look at other factors affecting sleep: sleep %3D Bo + Bitotwrk + Bzeduис + Взаде + m, where sleep and totwork (total work) are measure in minutes per week and educ and age are measured in years. (i) If adults trade sleep for work, what is the sign of B1? (ii) What signs do you think B2 and ß3 will have? (iii) Using the data in SLEEP75.RAW, the estimated equation is sleep = 3,638. 25-. 148totwrk – 11.13educ – 2. 20age + µi If someone works five hours per week, by how manu minutes is sleep predicted to fall? Is this a large tradeoff? (iv) Discuss the sign and magnitude of the estimated coefficient on educ. (v) Would you say totwrk, educ, and age explain much of the variation in sleep? What other factors might affect the time spent sleeping? Are these likely to be correlated with totwrk?

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)

Use Excel's regression function to develop a multiple regression forecast for Pricilla Phashions Indicate the r² of the model, and whether all the coefficients for the regression are significant, then calculate the dress sales for Year-3. show steps on excel

Please help me answer the attached questions. Thanking you

Don't use Ai

2. Suppose you run a regression with quantity as your dependent variable and advertising as one of your independent variables. The p-value on advertising is .08. The marketing team is arguing that their advertising efforts are impacting sales, but the finance/economics department is arguing that there isn’t evidence that the advertising is impacting sales. What side would you take and why? Note that this question squarely hits the idea that stats is part science/part art...

What is the solution?

Imagine you are an economist working for the Government of Econville. You are tasked with developing a model to predict the GDP of the country based on various factors such as interest rates, inflation, unemployment rate, and population growth. You collect quarterly data for the past 20 years and start building your model. After running your initial regression, you notice some peculiar patterns in the residuals: (1) residuals do not have identical variances across different levels of the independent variables; (2) two or more independent variables in a regression model are highly correlated with each other; (3) the correlation of a variable with its own past values. You suspect that your model might be suffering from 3 potential issues in the regression analysis that can affect reliability and validity. what are the implications of Heteroscedasticity if this potential issue in your model?

Imagine you are an economist working for the Government of Econville. You are tasked with developing a model to predict the GDP of the country based on various factors such as interest rates, inflation, unemployment rate, and population growth. You collect quarterly data for the past 20 years and start building your model. After running your initial regression, you notice some peculiar patterns in the residuals: (1) residuals do not have identical variances across different levels of the independent variables; (2) two or more independent variables in a regression model are highly correlated with each other; (3) the correlation of a variable with its own past values. You suspect that your model might be suffering from 3 potential issues in the regression analysis that can affect reliability and validity.  Based on Addressing Heteroscedasticity list one test you would employ to test this potential issue?

Imagine you are an economist working for the Government of Econville. You are tasked with developing a model to predict the GDP of the country based on various factors such as interest rates, inflation, unemployment rate, and population growth. You collect quarterly data for the past 20 years and start building your model. After running your initial regression, you notice some peculiar patterns in the residuals: (1) residuals do not have identical variances across different levels of the independent variables; (2) two or more independent variables in a regression model are highly correlated with each other; (3) the correlation of a variable with its own past values. You suspect that your model might be suffering from 3 potential issues in the regression analysis that can affect reliability and validity. List 2 factors in your model that might be causing the Heteroscedasticity and give a reason

Imagine you are an economist working for the Government of Econville. You are tasked with developing a model to predict the GDP of the country based on various factors such as interest rates, inflation, unemployment rate, and population growth. You collect quarterly data for the past 20 years and start building your model. After running your initial regression, you notice some peculiar patterns in the residuals: (1) residuals do not have identical variances across different levels of the independent variables; (2) two or more independent variables in a regression model are highly correlated with each other; (3) the correlation of a variable with its own past values. You suspect that your model might be suffering from 3 potential issues in the regression analysis that can affect reliability and validity. List 2 factors in your model that might be causing the Multicollinearity and give a reason

Imagine you are an economist working for the Government of Econville. You are tasked with developing a model to predict the GDP of the country based on various factors such as interest rates, inflation, unemployment rate, and population growth. You collect quarterly data for the past 20 years and start building your model. After running your initial regression, you notice some peculiar patterns in the residuals: (1) residuals do not have identical variances across different levels of the independent variables; (2) two or more independent variables in a regression model are highly correlated with each other; (3) the correlation of a variable with its own past values. You suspect that your model might be suffering from 3 potential issues in the regression analysis that can affect reliability and validity.  What name would you give to this potential issue that pertains to two or more independent variables in a regression model are highly correlated with each other