Newfood Case

Newfood campaign by Adrian Sanchez The coefficient of correlation between Price and gross gross sales is large and proscribe for all three-time periods. What does this say just about how wrongs Works? The correlation coefficient shows a whole t bingle of the linear relationship between these dickens unsettleds. However, this association does not refer causation, meaning that the change in one covariant is not caused by the change of the other one in the opposite direction. Yet, the increasing invalidating value of the correlation coefficients allows us to infer from these results that when the price rises sales go forth fall.This argument is supported by the level of significance of each good example less than 0,01. Explain the correlations between advertisement and sales. What is happening to the denote payoff over time? Apparently based solely on the correlation come the advertising has a negative effect on sales over the time. However when the level of signi ficance is analyzed, it turned evident that these numbers are way greater than the (0. 001) level of significance corresponding with a 99. % confident level. Hence they are not significant and it is safe to solve that the correlation numbers between advertising and sales have no effect. Note that the inter-correlations between advertising location and prices are all zero. wherefore? This result support the experiment parameters established from the beginning, we were considering this multivariates as independents, meaning that there are no linear relationship among them, endorsing the design of the experiment.What do the relapses of sales unsettleds (Sales1, Sales2, Sales3) using P, A and L as independent covariants, imply about the effect of prices? Of advertising? Of Location? Effect of Price As we declared in the question 1 there is a strong correlation between the prince and the sales numbers. An increment in price suggests a decrease in sales. So, based on this result, w e may say that the market is price sensitive and the company should take into consideration the price variable when growing the final launch plan of the product. Significance level is below 0. 1 meaning a 99% of confidence level. Effect of Advertising collect to a high significance level, p-value higher than 0. 01 not accomplishing the 99% or charge 95% of confidence level, we may safely state that advertising has no effect on sales. Effect of Location Due to a high significance level, p-value higher than 0. 01 not accomplishing the 99% or even 95% of confidence level, we may safely state that location has no effect on sales. Rerun adding income and volume. Do your judgments about the effect of price, advertising and location change? Why?When taking into consideration Income and Volume as additional values, my judgment does not change regarding the price and location effect. However, the jar of adding these two variables in the regression sample make the advertising variable to become significant, and then having an effect in the actual outcomes of sales. In fact, only the volume variable affect the advertising significance in this case, income variable is not significant at 99% confident level. After analyzing the correlation chart, we solidized that volume & advertising are correlated (negatively).So the regression model fails to predict accurately the effect of advertising on sales. Since we have two independent variables correlated, we need to control for volume and vary the advertising variable in order to get the real effect of this last one on the final outcomes of sales. What additional regression runs if any, should be made to finish the analysis of this data? I would run the regression of the 6 months sales compiled as dependent variable and the others variables as independent (i. e. Price, advertising, location, Income, Volume).I would also pick up deeper in the interaction between al the independent variables (Price, advertising, location, income and volume). It is very serious to understand the real effect of advertising in this model, for that as aforesaid(prenominal) we need to run model in which volume is controlled in different scenarios checking the behavior on the advertising in order to measure its real effect on sales. If possible obtain an output of residuals. Check the residuals to commit observations that do not seem to fit the model. Why dont they fit?They do not fit because perfectly because the initial regression model we are using is a linear model. Is very such(prenominal) likely that the relation between the independent variable and the dependent variable change the slope as the number increase or decrease forming a curve in a YX chart. However the linear theme seem to be very appropriate aft(prenominal) looking after the shape of the data in the chart. Finally each independent variable has a different effect over the dependent variable, which makes the residuals also different, when analyse a mong each other.