Insight on multiple regression
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Multiple regression allows examination of the linear relationship between one dependent variable (sales of bar) and two or more independent variables (price of bar and promotion expenditure). The multiple regression equation fits a plane to the data points to predict monthly sales based on price and promotion. The results show that price and promotion are significant predictors of sales, with an increase in price predicted to decrease sales and an increase in promotion predicted to increase sales. The model explains 75.8% of the variation in sales.
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- Time series data that records values over time can be used for forecasting future values. Examples include factory output per day and monthly sales.
- Plotting time series data helps identify trends like increasing, decreasing, or no trend over time. Regression analysis can also be used to identify linear trends and make predictions.
- Moving averages smooth out fluctuations by taking the average of values over a fixed time period, like 3 years. This helps identify trends more clearly.
- Components of a time series include trends, seasonal variations, cycles, and random variations. Additive and multiplicative models explain how these components combine
IPCC_Marginal Costing
Marginal costing is a technique that separates total costs into fixed and variable costs. It helps management make decisions by calculating indicators like profit volume ratio, break-even point, margin of safety, and indifference point. The document provides an example problem demonstrating how to use marginal costing to calculate these indicators and make decisions. It also discusses how marginal costing varies from other costing techniques.
Marginal Costing for IPCC
Marginal costing is a technique that separates total costs into fixed and variable costs. It helps management make decisions by calculating indicators like profit volume ratio, break-even point, margin of safety, and indifference point. The document provides an example problem demonstrating how to use marginal costing to calculate these indicators and make decisions. It explains key concepts like contribution, variable costs, fixed costs, and how marginal costing varies from other costing techniques.
Break even analysis_Numerical
This document provides information and examples about calculating break-even point for companies that produce multiple products. It defines weighted average selling price and variable expenses as the sales-weighted averages of individual product prices and expenses. Break-even point for multiple products is calculated as total fixed costs divided by weighted average contribution margin per unit. An example is provided to demonstrate calculating break-even point in both units and rupees for a company with three products. The document also discusses calculating an overall composite break-even point using total fixed costs divided by the composite profit/variable ratio.
Sppt chap007
1) The document discusses cost-volume-profit (CVP) analysis, which is used to determine the break-even point where revenues and expenses are equal.
2) It provides examples of how to calculate the break-even point using the equation approach, contribution margin approach, and contribution margin ratio for a surfboard company.
3) Graphs are used to illustrate the relationships between costs, sales, and profits at different production volumes.
Stat_AMBA_600_Problem Set3
The document describes a regression analysis conducted to determine the relationship between advertising costs and number of orders for a new diabetes drug. A strong positive correlation was found between the two variables (r=0.88093). The regression equation derived to predict advertising costs based on orders was y = 0.00971950x + 47895, with R^2 = 0.776. This indicates that 77.6% of the variation in advertising costs is explained by number of orders. Based on this strong correlation and the small standard error, the regression results provide sufficient evidence for the company to use in making decisions about next year's advertising budget.
Cvp & be analysis
This document discusses concepts related to cost-volume-profit analysis and break-even analysis. It defines marginal costing, contribution margin, profit-volume ratio, break-even point, and margin of safety. It also includes examples showing how to calculate these metrics using cost and revenue data. The document is intended to help managers understand how costs, sales volume, and price affect profitability.
Regression Presentation.pptx
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Insight on multiple regression
- 1. Multiple Regression S Vijay Ganesh
- 2. Multiple Regression Multiple Regression allows us to: Examine the linear relationship between 1 dependent (Y) & 2 or more independent variables (Xi) kik2i21i10i XβXβXββY ++++= Multiple Regression Model with k Independent Variables: Y-intercept Population slopes
- 3. Multiple Regression For example: Null Hypothesis: There is no relationship between Price of Bar and the Sales of bar in Store Null Hypothesis: There is no relationship between Promotion activity & Sales of bar in Store Independent Variables Price Promotion Dependent Variable Sales of Bar
- 4. Multiple Regression Bivariate regression is based on fitting a line as close as possible to the plotted coordinates of your data on a two-dimensional graph. Trivariate regression is based on fitting a plane as close as possible to the plotted coordinates of your data on a three-dimensional graph. Stores 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 Bars 4141 3842 3056 3519 4226 4630 3507 3754 5000 5120 4011 5015 1916 675 3636 3224 2295 2730 2618 4421 4113 3746 3532 3825 1096 761 2088 820 2114 1882 2159 1602 3354 2927 Price 59 59 59 59 59 59 59 59 59 59 59 59 79 79 79 79 79 79 79 79 79 79 79 79 99 99 99 99 99 99 99 99 99 99 Promoti on 200 200 200 200 400 400 400 400 600 600 600 600 200 200 200 200 400 400 400 400 600 600 600 600 200 200 200 200 400 400 400 400 600 600
- 5. Multiple Regression Sales (Y): Price (X1) Promotion Expenditure (X2): Y X1X2 0 What multiple regression does is fit a plane to these coordinates.
- 10. Output b0 = 5837.520, b1 =-53.217 , b2=3.6131 Therefore Multiple regression Equation is Yi = 5837.520-53.217X1i+3.6131X2i Where Yi= Predicated Monthly Sales of Bar for Store i X1i= Price of Bar ( in Cent) for Store i X2i=Monthly in Store Promotional Expenditure ( in Dollar) for Store i
- 12. Interpretation If the Price & Promotional Activity is Zero, Then the Estimated sold of bar is 5,537.5208. For a Given amount of Monthly Promotional Expenditure , Mean sales of bar are estimated to decrease by (b1=-53.2173) 53.2173 per month if Price of bar is increased by 1 Cent. For a Given Price, Mean sales of bar are estimated to Increased by (b2=3.6131) 3.6131 per month if Monthly Expenditure for Promotional activity is increased by 1 dollar. Standardized Beta Value: Change in outcome associated with a unit change in Predictor. Beta (Price)=-0.690 Mean Increase of Price by One std. deviation will reduce the Sale by std .dev of 0.690. Predicting Dependent Variable Y for store 1 & 17 For Store 1 ; X11=59, X21=200 Y1= 5837.5208-53.217(59)+3.613(200) Y1 = 3420.318 Bar For Store 17 ; X117=79, X217=400 Y17= 5837.5208-53.217(59)+3.613(200) Y17 = 3078.57 Bar
- 13. Interpretation The standard error is an estimate of the standard deviation of the coefficient. Here in Price, b1=-53.217 can vary 6.825 from the predicted price. Tstat = Coefficient of Constant/ Std.Error. P Significant Value 0.00 < 0.05. we can see that the predictor variables of Price & Promotion are significant because both of their p-values are 0.000.Hence you can reject the Null Hypothesis. We can be 95% confident that the real, underlying value of the coefficient that we are estimating falls somewhere in that 95% confidence interval To determine whether Price is having Significant effect on sales, by taking the Promotion expenditure in to account. We can Use Tstat=-7.766. T critical is calculated from Table E3 for df 31 are -2.0395+2.0395 Tstat(-7.766)> Tcritical (-2.0395). Hence reject Null Hypothesis & Concluding there is significant relationship between Sales & Price. To determine whether Promotion is having Significant effect on sales, by taking the Price in to account. We can Use Tstat=5.273 T critical for df 31 is calculated from Table E3 are -2.0395+2.0395 Tstat(5.273)> Tstat(2.0395). Hence reject Null Hypothesis & Concluding there is significant relationship between Sales & Promotion.
- 14. SSRSSR SSESSE SSTSST MSE=SSE/dfMSE=SSE/df MSR=SSR/dfMSR=SSR/df R Square =0.758 means 75.8% of variations in sales is explained by the variation in Price & Promotion Expenditure. Adjusted R Square =0.742 means 74.2% of Variations in sales is explained by variations in Prices & Promotion. Adjusted R Square is extremely Important when you are comparing two or more regression Model that predict same dependent variable but have a different Independent Variable. SSR/SSTSSR/SST
- 15. Using 0.05 level of significance, F critical is 3.32 calculated from E 5 table with df1 =2 & df2=31 Fstat (48.477)> Fcritical (3.32). Hence Reject Null Hypothesis. It is very clear that at least one independent variable is related to sales.
- 16. Increase of Price by 16.286 Cents will decrease the Sales of Bar by 0.690x1256.422 =867 Units Increase of Promotion by 162.862 Dollar will Increase Sales of Bar by 0.468*1256.422=588 Units Interpretation of Standardized Beta
- 17. Thank you