A regression line has an intercept value of 174.71 and slope value of 25.22. What is the predicted value (the y-value) in the 100th period (the x-value is 100)? Round to two decimal places.
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A regression line has an intercept value of 174.71 and slope value of 25.22. What is the predicted value (the y-value) in the 100th period (the x-value is 100)? Round to two decimal places.
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- Calculate the standard deviation of the following returns. Year Return 1 0.01 2 0.27 3 0.02 4 0.02 5 0.22 Enter the answer with 4 decimals, e.g. 0.1234.A linear regression model is Units = 4,004 – 0.659×Week. For week 46, what is the forecast for the number of units? Round your answer to the nearest whole number. unitsRound each z-score to the nearest hundredth.A data set has a mean of x = 6.2 and a standard deviation of 2.2. Find the z-score for each of the following. (a) x = 6.2(b) x = 7.2(c) x = 9.0(d) x = 5.0
- TSLAZ had returns of 0.03, 0.06, 0.12 and -0.1 during the last four "periods". Can you calculate the standard deviation of the returns? Type your answer as decimal (i.e. 0.052 and not 5.2%). Round your answer to the nearest four decimals if needed.Calculate the standard deviation of the following returns. Year Return 1 0.25 2 0.16 3 0.01 4 0.07 5 -0.11Calculate the arithmetic average of the following returns. Year Return1 0.12 20.16 3 0.24 4 0.13 50.02 Enter the answer with 4 decimals, e.g. 0.1234.
- The geometric average of -12%, 20%, and 27% is __________.(Type your answer as a percent, two decimal places, e.g. "10.50" for 10.5%)Calculate the geometric mean return for the following data set: (Negative value should be indicated by a minus sign. Round your intermediate calculations to at least 4 decimal places and final answer to 2 decimal places.) -14% 15% -16% 13.7% Geometric mean return 14.1% %The following 20 observations are for two quantitative variables, x and y. a. Create a scatter chart for these 20 observations. b. Fit a linear trendline to the 20 observations. What can you say about the relationship between the two quantitative variables?
- Consider the following time series data: Construct a time series plot. What type of pattern exists in the data? Use a multiple regression model with dummy variables as follows to develop an equation to account for seasonal effects in the data: Qtr1 = 1 if quarter 1, 0 otherwise; Qtr2 = 1 if quarter 2. 0 otherwise; Qtr3 = 1 if quarter 3, 0 otherwise. Compute the quarterly forecasts for next year based on the model you developed in part (b). Use a multiple regression model to develop an equation to account for trend and seasonal effects in the data. Use the dummy variables you developed in part (b) to capture seasonal effects and create a variable t such that t = 1 for quarter 1 in year 1, t = 2 for quarter 2 in year 1, … t = 12 for quarter 4 in year 3. Compute the quarterly forecasts for next year based on the model you developed in part (d). Is the model you developed in part (b) or the model you developed in part (d) more effective? Justify your answer.Consider the following time series: a. Construct a time series plot. What type of pattern exists in the data? Is there an indication of a seasonal pattern? b. Use a multiple linear regression model with dummy variables as follows to develop an equation to account for seasonal effects in the data: Qtr1 = 1 if quarter 1, 0 otherwise; Qtr2 = 1 if quarter 2, 0 otherwise; Qtr3 = 1 if quarter 3, 0 otherwise. c. Compute the quarterly forecasts for next year.What is the standard deviation of a random variable q with the following probability distribution? (Do not round intermediate calculations. Enter your answer in numbers not in percentage. Round your answer to 4 decimal places.) Value of q 0 1 2 Probability 0.30 0.22 0.48 Standard deviation