DA 662 Exam 2 Forecasting Spring 24'

The following gives the number of accidents that occurred on Florida State Highway 101 during the last 4​ months:     Jan    Feb    Mar    AprMonth    1    2    3    4Number of Accidents    30    40    70    105 Using the least-squares regression ​method, the trend equation for forecasting is ​(round your responses to two decimal ​places)​: y = ? + ?x

You own a restaurant near the beach. Business has been growing each year, but obviously spikes during the summer months. A regression produces the following equation: M = 30,000 + 530t + 1,000S Where M is monthly sales, t is years past 2010, and S is a dummy variable for the summer months. If the month is June, July, or August, insert a "T". If not, the value for S is zero. What are the predicted sales for June 2026? Enter as a value.

With a new chef and a creative menu, Café Venetian has witnessed a huge surge in sales. The following data show a portion of daily sales (in $) at Café Venetian in the first 100 days after the changes. Day Sales 1 226 2 185 100 1,737 pictureClick here for the Excel Data File a-1. Estimate the exponential trend model. (Negative values for regression coefficients should be indicated by a minus sign. Round your answers to 2 decimal places.) Predictor Variable Constant Day Coefficient 0.00 a-2. Use the estimated model to forecast for the 101st day. (Do not round coefficient estimates. Round final answer to 2 decimal places.) Forecast for the 101 day

You own a restaurant near the beach. Business has been growing each year, but obviously spikes during the summer months. A regression produces the following equation: M = 30,000 + 500t + 1,000S Where M is monthly sales, t is years past 2010, and S is a dummy variable for the summer months. If the month is June, July, or August, insert a "1"”. If not, the value for S is zero. What are the predicted sales for July 2020? Enter as a value.

d. If the director used these 4 weeks of data to create a linear regression, what does that linear regression formula suggest for this week's forecast of employee appointments? What does the regression analysis suggest in general about employee appointments for Director Very Busy?

Given the following summary statistics, determine the regression equation used to predict y from Ta Round all answers to 2 decimal places. slope - y-intercept Sy SI T 15 Y 1.02 1.6 -0.71 20.65 77-9 Use the exact value of slope when calculating the y-intercept.

6

You estimated the following regression.         Source |       SS           df       MS      Number of obs   =       117 -------------+----------------------------------   F(1, 115)       =      0.06        Model |  2523.61281         1  2523.61281   Prob > F        =    0.8003     Residual |  4513680.09       115  39249.3921   R-squared       =    0.0006 -------------+----------------------------------   Adj R-squared   =   -0.0081        Total |   4516203.7       116  38932.7906   Root MSE        =    198.11   ------------------------------------------------------------------------------            Y |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval] -------------+----------------------------------------------------------------            X |   40.18036   158.4597     0.25   0.800    -273.6979    354.0586        _cons |  -556.9531   11761.12    -0.05   0.962    -23853.48    22739.57 ------------------------------------------------------------------------------…

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

Solve for the coefficient of correlation

solve within 30 mins.

Year Quarter Revenue Q1 4.80 Q2 4.56 2015 Q3 4.88 Q4 4.91 Q1 5.37 Q2 4.99 2016 Q3 5.24 Q4 5.71 Q1 5.73 Q2 5.29 2017 Q3 5.66 Q4 5.70 Q1 6.07 Q2 6.03 2018 Q3 6.31 Q4 6.30 Q1 6.60 Q2 6.31 2019 Q3 6.82 Q4 6.75 Q1 7.10 Q2 6.00 2020 Q3 4.20 Q4 6.20

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 table lists fossil fuel production as a percentage of total energy production for selected years. A linear regression model for this data is (A) Draw a scatter plot of the data and a graph of the model on the same axes. y = - 0.33x+95.0 OA. OB. where x represents years after 1960 and y represents the corresponding percentage of oil imports. 100 100 Fossil Fuel Production Production (%) 96 Year 1960 07 -> 1970 1980 91 60 60 Years after 1060 88 Years after 1980 1990 84 OC. OD. 2000 83 100 , 100 0- 04 60 60 Years after 1960 Years after 1960 (B) Interpret the slope of the model. The rate of change of the percentage of oil imports with respect to time is -0.33% per year. (C) Use the model to predict fossil fuel production in 2010. In 2010 fossil fuel production as a percentage of total production will be about 78.5 %. (Round to one decimal place as needed.) (D) Use the model to estimate the year in which fossil fuel production will fall below 70% of total energy production. In the year…

Hi this question may have been posted before do not know just looking for assistance and having difficulties, the question comes from my online discussion forum post (SCMG 305 Global demand management)   Investigate the cause-and-effect relationships utilizing regression analysis, find one authoritative resource in the form of a U-tube video or Website that explains the use of regression analysis as a prediction model for forecasting. Try not to duplicate a resource already posted by another student. Insert a hyperlink for that resource so others may access it quickly. Finally, provide a quick short paragraph/summary explaining what you learned from the resource you provided strengthening your understanding of the use of regression analysis for forecasting.

Given the regression equationY = -50 + 12Xa. What is the change in Y when X changes by +3?b. What is the change in Y when X changes by -4?c. What is the predicted value of Y when X = 12?d. What is the predicted value of Y when X = 23?e. Does this equation prove that a change in X causes a change in Y?

Don't use Ai

In your company (electrical goods manufacturing), you want to forecast demand as well of a particular range of Smart TVs. Its monthly sales previous year, are presented in the table below: Month Sales January 400 February 650 March 1,150 April 1,700 May 500 June 900 July 1,150 August 1900 September 600 October 650 November 1,600 December 2050 Based on the above historical sales data: A) Create the corresponding graph and comment on the demand (sales) in terms of in trend, seasonality and periodicity please draw the diagram!!!!

1. Suppose that you have following data: Variable Description CEO salary measured in thousands of $ Firm's sale measure in millions ofS Return on equity in percent Salary sales roe *Return on equity is a measure of financial performance calculated by dividing net income by shareholders' equity. Your estimated regression is given by log (salary) = 4.322 + 0.276 log(sale) + 0.0215roe - 0.0008roe?, R = 282, n = 209. (324) (0.033) (0.0129) (0.00026) a) Is the effect of all independent variables statistically equal to 0? b) Interpret the coefficient on log(sale). c) Interpret the effect of roe on log(salary). • Without more information, your interpretation of the effect of roe on log(salary) should include answers to these sub-question. Should the roe be included in this model? il. Comment on relationship between roe and log(salary): is it U-shaped or inverse U-shaped? What is the turning point? How would you interpret this point? Plot log(salary) vs roe. v. ii. iv. Compute predicted value…

Q3. You are working as a researcher in an economic Institute, you want to study the relation between the Unit sales as a Dependent variable and the following independent variables (selling expenditure, advertising, competitive price) As shown in the following model Unit Sales + = b0+b1 Exp + + b2 Adv t b3 t+ compt + Ut After collecting your data, and estimating your linear regression over the data, you got the following regression equation comp t Unit Sales t = -10.5 - 0.51 Exp + + 0.09 Adv 3.05 b3 t + (2.45) (-1.5) t- value (4.2) (2.94) R² = 0.24 F- Value 0.33 ' 1- What is the economic meaning of the coefficient b0 (-10.5) 2- Describe the meaning of R² and its value, F - Value 3- What do you think about the Model as a whole, with F, R2 values....is it significant or not ....explain your answer

18. A multiple regression model, K = a + bX + cY + dZ, is estimated regression software, which produces the following output: D. If X equals 50, Y equals 200, and Z equals 45, what value do you predict K will take?

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.…

Do not solve list all the variables objective function and contraints                                  ASW Publishing, Inc., a small publisher of college textbooks, must make a decision regarding which books to publish next year. The books under consideration are listed in the following table, along with the projected three­year sales expected from each book.     Book Subject Type of Book Projected Sales (000s)   Business calculus New 20   Finite mathematics Revision 30   General statistics New 15   Mathematical statistics New 10   Business statistics Revision 25   Finance New 18   Financial accounting New 25   Managerial accounting Revision 50   English literature New 20   German New 30   The books that are listed as revisions are texts that ASW already has under contract; these texts are being considered for publication as new editions. The books that are listed as new have been reviewed by the company, but contracts have not yet been signed.The company has three individuals who can…

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?

calculate b

which sentences are correct? 1.Decomposition methods assume that the actual time series value at period t is a function of three components: trend, seasonal, and irregular. 2.Dummy variables can be used to deal with categorical independent variables in a multiple regression model. 3.If a time series exhibits a linear trend, the method of least squares may be used to determine a trend line (projection) for future forecasts. 4.Time series decomposition can be used to separate or decompose a time series into seasonal, trend, and irregular (error) components. 5.A variety of nonlinear functions can be used to develop an estimate of the trend in a time series, including quadratic trend equation and exponential trend equation. 6.Hypothesis Testing about the variances of Two Populations apply with F test Statistic. 7.Hypothesis Testing about the variances of One Populations apply with Chi test Statistic. 8.Hypothesis Testing about the variances of One Populations apply with F test Statistic.…

Given the regression equationY = 43 + 10Xa. What is the change in Y when X changes by +8?b. What is the change in Y when X changes by -6?c. What is the predicted value of Y when X = 11? d. What is the predicted value of Y when X = 29? e. Does this equation prove that a change in X causes a change in Y?

16 The predicted WAGE when EDUC = 16 is, a $25.43       b $24.22       c $23.06       d $21.97