To: The Regional Food Marketer Date: November 4, 2012
Ye Olde FoodKing Company
From:
Re: Big Suzy’s Snack Cakes Regression Analysis
Introduction
The Regional Food Manager for Ye Olde FoodKing Company has retained Mark Craig of Blue Steel Consulting to perform a regression analysis to forecast demand of your product. The four characteristics readily available included price, competitors’ price, average income, and market population. The results of each regression analysis are presented at the end of this memo. The remainder of this memo describes the regression analysis used and limitations to the data available. Running a regression provides a statistical procedure to estimate the liner dependency of one or more
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Key Regression Analysis Factors
The following factors are the most commonly reviewed results of a regression analysis:
* Correlation coefficient (R-squared) – This represents how well the independent variables (X) explain the response variable (Y). * Independent variable coefficient – This is the measured effect the independent variables have on the dependent variable. This is the main output of the regression analysis. * Statistical significance of the coefficient – This is a statistical test that confirms if the coefficient regardless of its value is robust and different from zero. Also referred to as the P-value.
Statistical significance of the coefficient
The statistical significance of a coefficient tests determines coefficients potential of being zero. The zero potential increases when there is significant variance in the independent variables. A large variance also suggests that the variable used have no effect on the dependent variable.
Market Performance
As you can see from the previous explanation, it shows that our regression model is highly effective and explains the variation in the number of pies you sold from market to market. The limitation of regression analysis can be described in terms of regression, correlation, and causation. Regression and correlation are related but describe
Meaning is there some degree of significant when I visually analyze the regression correlation. This allows me to establish a quantitative relationship between two or more variables. The independent variables (LHS) are used to predict, while the dependent variable (RHS) is used to response to the independent variable. To get the best fitted line of regression the best method of least squares. This is done by minimizing the sum of squares of vertical deviation, because since the deviation is squared followed by summed there is no indication of positive and negative values. Once I fitted my regression model, then I will analyze my residuals. The residuals are my deviations from the observation values. This will determine if my assumption on the linear relationship is feasible.
Data that is statistically significant helps us to understand whether there is a relationship within the null hypothesis. T statistical significant results showed that the participants who consumed caffeine performed worse than participants who did not was less than 5% (p < .05). This means for the author to determine whether a test is significant it must be less than .05, which is the significance level. The P value (probability value) is defined as the probability of obtaining a result equal to or "more extreme" than what was observed (Wikipedia). A p-value helps determine the significance
There is a null hypothesis and an alternative hypothesis. The null hypothesis usually states there is no difference and an alternative hypothesis states there is. A result is positive if it rejects the null hypothesis. A result is negative if it does not reject the null
Table~\ref{tab:example1} shows the coefficient for Batting Average which is 5.4, this mean that 1 unit of increase in batting average produce a 5.4 increase in balco scandal variable. If our measure of batting average is equal to zero, we would expect the efect of steroides (balco scandal) decrease in a 1.5\%. The t value calculated is greater than the critical value of t thus we can reject the null hypothesis in this case. The p value shows a statistical significance at 95\% of confident.
For the analysis the packaged food company ConAgra Foods, Inc (CAG) was chosen. According to ConAgra 2013 Annual report, ConAgra Foods, Inc. is one of the USA’s leading food companies. It has a strong brand recognition and consumer loyalty. ConAgra 's products are sold both in large supermarkets and convenience stores. Company operates in Commercial and Consumer Foods segments. The food industry is especially interesting for the research as the demand on food will stay relatively stable even during economic crises and is continuously growing.
A Walmart in Louisiana is apologizing for making a cake inspired by the ISIS flag. A man went into the Walmart store asking to have a cake made to mimic the confederate flag. On the cake he wanted the words "Heritage not hate". He was later notified that the store would not make this cake. Walmart has recently stated that they will no longer carry confederate flag merchandise.
The regression analysis was initially run using all variables to determine the significance of each when associated
The coefficient of determination or r2: It determines the proportion of variation in the dependent variable by the independent variable.
Colors tend to be darker in the red spectrum, with some exceptions of white, green and purple
Analyzing the correlation matrix of the independent variables. If the correlation between independent variable is fairly high (generally above 0.90), then this is an indication multicollinearity. Multicollinearity can be appear due to the combined effect of two or more independent variables.
Chocolate Chip Oat No-Bake Cookies These are made on the stovetop but they’re quick and easy—and very good. We can make them in less than ten minutes from assembled ingredients to cookies on the sheet. Everyone likes a chocolate chip cookie. The oats make it a chewy chocolate chip cookie. It’s a wholesome cookie that you won’t mind including in your child’s lunch box.
He was looking around the Butter Bar and admiring all of the beautiful pictures on the walls. In an instant,
Telling stories with photographs is a way for photographers to learn to create narratives. After showing you an example here recently, with birds, let me share another one now, with a cat and a cake.
From the table 6 of analysis, it can be observed that there were negative returns on manufacturing as the coefficient of variable C is -0.0006 but this return is not significant as p value is 0.6893. However, there were positive returns on these stocks as dummy variable coefficient shows positive value of 0.003311 and is not significant as p value of dummy variable is 0.1204 which is greater than the significance level of 0.05.So, in that case, null hypothesis H0 has been accepted.