30-3quarter sales1801681591751234567790.1O 0.5O190Determining the MAD, MSE and MAPE using exponential smoothingmethod with smoothing constatnt alpha -0.1 and 0.5. Which one leads tosmaller forecast error?205180182?000000F4F5MacBook44F7DIIFB

Given time series data set is:QuarterSales118021683159417551906205718081829?For a given exponential…

Answered: quarter sales 180 168 159 175 190 1 2 3…

Elementary Linear Algebra (MindTap Course List)

8th Edition

ISBN:9781305658004

Author:Ron Larson

Publisher:Ron Larson

Chapter5: Inner Product Spaces

Section5.CR: Review Exercises

Problem 62CR

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Olympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s pole vault height has fallen below the value predicted by the regression line in Example 2. This might have occurred because when the pole vault was a new event there was much room for improvement in vaulters’ performances, whereas now even the best training can produce only incremental advances. Let’s see whether concentrating on more recent results gives a better predictor of future records. (a) Use the data in Table 2 (page 176) to complete the table of winning pole vault heights shown in the margin. (Note that we are using x=0 to correspond to the year 1972, where this restricted data set begins.) (b) Find the regression line for the data in part ‚(a). (c) Plot the data and the regression line on the same axes. Does the regression line seem to provide a good model for the data? (d) What does the regression line predict as the winning pole vault height for the 2012 Olympics? Compare this predicted value to the actual 2012 winning height of 5.97 m, as described on page 177. Has this new regression line provided a better prediction than the line in Example 2?
What does the y -intercept on the graph of a logistic equation correspond to for a population modeled by that equation?
Interpret the least squares regression line of this data set. Meteorologists in a seaside town wanted to understand how their annual rainfall is affected by the temperature of coastal waters. For the past few years, they monitored the average temperature of coastal waters (in Celsius), x, as well as the annual rainfall (in millimetres), y. Rainfall statistics • The mean of the x-values is 11.503. • The mean of the y-values is 366.637. • The sample standard deviation of the x-values is 4.900. • The sample standard deviation of the y-values is 44.387. • The correlation coefficient of the data set is 0.896. The correct least squares regression line for the data set is: y = 8.116x + 273.273 Use it to complete the following sentence: The least squares regression line predicts an additional annual rainfall if the average temperature of coastal waters increases by one degree millimetres of Celsius.
The following gives the number of accidents that occured on Floridaq State highway 101 during the last 4 months" month jan feb mar apr # of accidents 30 45 60 95 Using the least-squares regression method the trend equation for forecasting is (round your responses to two decimal places): ^Y=[__]+[___]X
Interpreting technology: The following display from the TI-84 Plus calculator presents the least-squares regression line for predicting the price of a certain stock y from the prime interest rate in percent x. LinReg y=a+bx a=2.29525776 b=0.38970413 r^2=0.4319044662 r=0.65719439 Write the equation of the least-squares regression line. Use the full accuracy shown in the calculator output (do not round your answers).
Interpreting technology: The following display from the TI-84 Plus calculator presents the least-squares regression line for predicting the price of a certain stock y from the prime interest rate in percent x LinReg y=+a+bx a=2.29525776 b=0.38970413 r^2=0.4319044662 r=0.65719439 Predict the price when the prime interest rate is 6%. Round the answer to at least four decimal places.
Use the least squares regression line of this data set to predict a value. Meteorologists in a seaside town wanted to understand how their annual rainfall is affected by the temperature of coastal waters. For the past few years, they monitored the average temperature of coastal waters (in Celsius), x, as well as the annual rainfall (in millimetres), y. Rainfall statistics • The mean of the x-values is 11.503. • The mean of the y-values is 366.637. • The sample standard deviation of the x-values is 4.900. • The sample standard deviation of the y-values is 44.387. • The correlation coefficient of the data set is 0.896. The least squares regression line of this data set is: y = 8.116x + 273.273 How much rainfall does this line predict in a year if the average temperature of coastal waters is 15 degrees Celsius? Round your answer to the nearest integer. millimetres
A logistic regression model was proposed for classifying common brushtail possums into their two regions. The outcome variable took value 1 if the possum was from Victoria and 0 otherwise. Estimate SE Pr(>|Z|) (Intercept) 31.1256 9.7822 3.1819 0.0014 sex_male -1.3917 0.6456 -2.1557 0.0312 -2.2381 skull_width total_length tail_length -0.2782 0.1243 0.0252 0.5529 0.1337 4.1354 -1.8147 0.3636 -4.9909 (a) Write out the form of the model. log_odds(Victoria) =| sex_male skull_width + total_length + tail_length Also identify which of the variables are positively associated when controlling for other variables. O skull_width O sex_male total_length tail_length (b) Suppose we see a brushtail possum at a zoo in the US, and a sign says the possum had been captured in the wild in Australia, but it doesn't say which part of Australia. However, the sign does indicate that the possum is male, its skull is about 64 mm wide, its tail is 40 cm long, and its total length is 85 cm. What is the reduced…
7
The following result perspective in RapidMiner shows a multiple linear regression model. Based on the diagram, the model for our dependent variable Y is Predicted Y= (Insulation *0.420)+(Temperature *0.071)+(Avg_Age*0.065)+(Home_Size *0.311)+7.589 Attribute Insulation Temperature Avg Age Home Size (Intercept) O True O False Coefficient 3.323 -0.869 1.968 3.173 134.511 Std. Error 0.420 0.071 0.065 0.311 7.589 Std. Coefficient 0.164 -0.262 0.527 0.131 ? Tolerance 0.431 0.405 0.491 0.914 ? t-Stat 7.906 -12.222 30.217 10.210 17.725
I ONLY NEED PART C,D, and E answered please thanks A regression was run to determine if there is a relationship between the happiness index (y) and life expectancy in years of a given country (x).The results of the regression were: ˆyy^=a+bxa=-1.102b=0.074 (a) Write the equation of the Least Squares Regression line of the formˆyy^= + x(b) Which is a possible value for the correlation coefficient, rr? -0.858 -1.07 1.07 0.858 (c) If a country increases its life expectancy, the happiness index will decrease increase (d) If the life expectancy is increased by 2.5 years in a certain country, how much will the happiness index change? Round to two decimal places._____(e) Use the regression line to predict the happiness index of a country with a life expectancy of 63 years. Round to two decimal places._______Use the space below to type your answer AND/OR to upload a picture of your work for all the questions in this problem.
Explain the concept of Asymptotic Distribution of the OLS Estimatorand t-Statistic in the multi regression model?

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quarter sales 180 168 159 175 190 1 2 3 st 4 5 6 7 7 9 205 180 182 Determining the MAD, MSE and MAPE using exponential smoothing method with smoothing constatnt alpha -0.1 and 0.5. Which one leads to smaller forecast error? O 0.1 O 0.5