Intro The introduction should describe the objective of this report. say the main purpose and/or findings from your analysis so the reader can easily pick out the purpose of this document. Please note that it is okay if this section feels redundant with information that is expanded upon later. It is like a summary of what the reader can expect. Data Analysis (Please be sure to include each of the three subheadings, presented here in italics.) Descriptive Statistics for the Price of the Wines

In this section describe the nature of the data, including the independent and dependent variables being explored. The mean is 31 which is bigger than the mode of 20, this shows us the skew of the histogram is right skewed/positive. What does this tell us about wine prices?

Descriptive Statistics for the Ratings of the Wines In this section describe the nature of the data, including the independent and dependent variables being explored. Place the descriptive statistics output here as well as histogram just as you did in the previous section. Discuss the mean, median, mode, and skew of the histogram. What does this tell us about wine ratings?

Regression Analysis Predicting Price Based on Ratings (Points) In this section describe the regression model and what it suggests. Start by presenting the scatterplot with the regression equation, the trendline, and the value for r2. Also include the Excel output of your regression analysis. Your name should be embedded in both figures.

In this section, be sure that you explicitly interpret the y-intercept coefficient of regression (b) coefficient of correlation (r) coefficient of determination (r2) predicted score for a specific value of x (choose a specific ratings value and predict a price) Summarize finding in terms of recommendations for upper management In the final summary section, it is okay if this section feels redundant with information already presented. This is summarizing the main points of your findings. Typically, this is about three short sections. Contains brief (1-2 sections) description of the data and how to better understand the relationship between ratings and price and includes interpretation of slope, correlation coefficient and regression coefficient Conclusion includes comments about anything you think is surprising, concerns you, or you think is generally interesting or might be useful for upper management. Conclusion includes a summary of your findings (takeaway message) that describes how the management might be able to use these results to price their wine

this tell us about wine ratings? Frequency Distribution for the Ratings Data Ratings in Points for US Wines $100 or Less Created by: Becca Stutler for US Wines Priceed at 100 Dollars or Less Mean 87.741421 Created by: Becca Stutler Standard Error 0.01357858 16000 Median 87 14000 Mode 87 Standard Deviation 3.35904462 12000 Sample Variance 11.2831808 10000 Kurtosis -0.5588477 8000 Free Skewness 0.11924436 6000 Range 20 4000 Minimum 80 2000 Maximum 100 Sum 5369424 80 82 84 86 88 90 92 94 96 98 100 Count 61196 Ratings for US Wines Regression Analysis Predicting Price Based on Ratings (Points) In this paragraph describe the regression model and what it suggests. Start by presenting the scatterplot with the regression equation, the trendline, and the value for r2. Also include the Excel output of your regression analysis. Your name should be embedded in both figures. OuttillIaly VI What we reader can expect. Data Analysis (Please be sure to include each of the three subheadings, presented here in italics.) Descriptive Statistics for the Price of the Wines In this paragraph described the nature of the data, including the independent and dependent variables being explored. Discuss the mean, median, mode, and skew of the histogram. What does this tell us about wine prices? Price for US Wines $100 or Less created by: Becca Stutler Mean 31.8594189 Standard Error 0.07196231 Frequency Distribution of Pricing Data for US Median 28 Wines Priced at $100 or Less Mode 20 Created by: Becca Stutler Standard Deviation 17.8019102 20000 Sample Variance 316.908009 18000 16000 Kurtosis 1.63442599 14000 12000 Skewness 1.25089888 10000 8000 Range 96 6000 Minimum 4000 2000 Maximum 100 1949669 10 20 30 40 50 60 70 80 90 100 Sum Price of US Wines Count 61196 Predicting Price Based on Ratings y = 2.6852x - 203.74 (created by: Becca Stutler) R2 = 0.2567 120Predicting Price Based on Ratings y = 2.6852x - 203.74 (created by: Becca Stutler) R2 = 0.2567 120 100 80 Price 60 40 20 70 75 80 85 90 95 100 105 1 10 Rating SUMMARY OUTPUT created by: Becca Stutler Regression Statistics Multiple R 0.50666552 R Square 0.25670995 Adjusted R Square 0.25669781 Standard Error 15.3479125 Observations 61196 ANOVA df SS MS F Significance F Regression 1 4978423.75 4978423.75 21134.561 0 Residual 61194 14414761.8 235.558418 Total 61195 19393185.6 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept -203.74143 1.62180358 -125.62645 0 -206.92017 -200.56269 -206.92017 -200.56269 Points 2.68517248 0.01847037 145.377306 0 2.64897051 2.72137446 2.64897051 2.72137446 In this section, be sure that you explicitly interpret the o y-intercept coefficient of regression (b)