Which of the following statements are true about correlation? A. The correlation describes the tendency of two data series to move together. B. A negative correlation indicates that two data series do not covary with each other. C. The correlation coefficient mesuares the degree of correlation between the two data series. D. A strong positive correlation or strong negative correlation provides evidence of covariation of two data series. E. A positive correlation indicates that two data series covary with each other.
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- 1. Why is the existence of a correlation (existence of co-occurrence or association) between and Fnot enough evidencefor us to say that Ycauses Y?2. What is the post hoc, ergo propter hoc fallacy? Give an exampleGiven the following data X (consumers of teff) or popn 3 6 8 1 13 13 14 Y ( teff consumption) 8 6 10 12 12 14 20 year 2013 2014 2015 2016 2017 2018 2019 Estimate the regression equation, Y= a+bX, Where Y denotes demand for teff while X is consumers of teff (population) By assuming demand for teff is only affected by its consumers, find the amount demand for teff in the year 2022 if the populations (consumers of teff) are about 18 people? (Hint: use the least square method, parameter a and b can be estimated by solving the two linear equations) SY= na+ bSX SXY=aSX +b Where n is number of years. For example, Estimate the sales for 2012, 2015 and fit a linear regression equation and draw a trend line.ar X Sales (Y) XY X2 year X Sales (Y) XY X2 2002 1 22734 22734 1 2003 2 24731 49462 4 2004 3 31489 94467 9 2005 4 44685 178740 16 2006 5 55319…Suppose in our model, lifespans increased due to a productivity in- crease. What would likely happen to hours worked and leisure over the lifetime of a representative consumer. (a) Let’s consider one of two common ways of implementing this change. Suppose the increase raised both lifespans and the amount of years consumers are healthy enough to work by the same amount, how would that likely affect the measured numbered of hours work by prime aged adults within a given year? What would likely hap- pen to the retirement age? (b) Let’s consider the other way to implement it. Suppose the change in lifetimes came about changes in technology that delayed death but did not extend the amount of years consumers are healthy enough to work. What would likely happen to hours worked within a year? What would happen to the retirement age? (c) In the US, the generalized stylized fact is that the actual age of retirement is not increasing. Nor are hours worked per week. Why are both of the…
- Suppose that a coffee producing firm estimated the following regression of thedemand for its brand of coffee:Qc = 1.5 − 3.0Pc + 0.8Y + 2.0Pb − 0.6PS +1.2 Awhere Qc = sales of coffee brand C, in dollarsper pound Pc = price of coffee brand C,in dollars per poundY = personal disposable income, in millions of dollars per yearPb = price of the competitive brand of coffee, in dollarsper pound Ps = price of sugar, in dollars per poundA = advertising expenditures for coffee brand C, in hundreds of thousands ofdollars per year.Suppose also that this year, Pc = $2, Y = $2.5, Pb = $1.80,Ps = $1 and A =$1.a. Interpret the results of the estimated demand.b. Compute point price elasticity of demand for the firm’s brand of coffeewith respect to its price.c. Compute the cross-price elasticity of demand for coffee with respect to theprice of competitive coffee brand b.d. At the current price level, would it be viable for the firm to increase the pricelevel of its brand of coffee? Support your answer.…What is Heteroscedastic? And is there a way to fix data in case of heteroscedasticity presence ?Pls help with below homework-) Which are the three methods to handle the panel data model with fixed effects ? Also explain each method
- 3. Rubax, a U.S. manufacturer of athletic shoes, estimates the following linear trend model for shoe sales: Qt = a+bt+c1D1+c2D2+c3D3Where, Qt = sales of athletic shoes in the t-th quartert = 1, 2, . . . . , 28 [2011(I), 2011(II), . . . . , 2017(IV)]D1 = 1 if t is quarter I (winter); 0 otherwiseD2 = 1 if t is quarter II (spring); 0 otherwiseD3 = 1 if t is quarter III (summer); 0 otherwiseThe regression analysis produces the following results: Is there sufficient statistical evidence of an upward trend in shoe sales? explain Do these data indicate a statistically significant seasonal pattern of sales for Rubax shoes? If so, what is the seasonal pattern exhibited by the data? Using the estimated forecast equation, forecast sales of Rubax shoes for 2018(III) and 2019(II).Write first-order model relating E(y) to four quantitative independent variables.Explain the relationship that economic variables can have with businesscycles. These have both direction and timing. Give an example economicvariable of each combination of direction timing. Of these two attributes, ifyou had exclusive access to a reliable variable, which relationships wouldyou want this variable to have for forecasting purposes
- Suppose that the government tends to maximize the tax revenue. How would the cannabis price drop affect the government tax revenue if cannabis was taxed by price? Did the government anticipate the cannabis price drop when designing the tax scheme? You may come up with an artificial numerical example to support your findings. are alcohol and cigarettes substitutes or complements of cannabis?(The answer to this question depends on the actual consumption data. If you do not know the data, you may make an assumption. Then, you should stick with this assumption for the rest of your analysis. For example, if you assume that alcohol and cigarettes are substitutes of cannabis, then your entire analysis is based upon this assumption.) How would the change in the price of cannabis possibly affect the market demand curve for alcohol/cigarettes?Q. Rubax, a U.S. manufacturer of athletic shoes, estimates the following linear trend model for show sales: Qt = a + bt + c1D1 + c2D2 + c3D3 Qt = sales of athletic shoes in the tth quarter t = 1, 2,..., 28 [2014(I), 2014(II),....,2020[IV)] D1 = 1 if t is quarter I (winter); 0 otherwiseD2 = 1 if t is quarter II (spring); 0 otherwiseD3 = 1 if t is quarter III (summer); 0 otherwise The regression analysis produces the following results: a. Is there sufficient statistical evidence of an upward trend in shoe sales? b. Do these data indicate a statistically significant seasonal pattern of sales of Rubax shoes? If so, what is the seasonal pattern exhibited by the data? c. Using the estimated forecast equation, forecast sales of Rubax shoes for 2021(III) and 2022(II). d. How might you improve this forecast equation? Thank you!Table 3.8 gives data on gross domestic product (GDP) for the United States for the years 1959-1997. 1. Plot the GDP data in current and constant (i.e., 1992) dollars against time. 2. Letting Y denote GDP and X time (measured chronologically starting with 1 for 1959, 2 for 1960, through 39 for 1997), see if the following model fits the GDP data: Yt = β1 + β2 Xt + ut Estimate this model for both current and constant-dollar GDP. 3. How would you interpret β2? 4. If there is a difference between β2 estimated for current-dollar GDP and that estimated for constant-dollar GDP, what explains the difference? 5. From your results what can you say about the nature of inflation in the United States over the sample period?