Analytics Report

ANALYTICS REPORT TO: E. AND J. GALLO WINERY FROM: MADDY RAKER SUBJECT: CASE ASSIGNMENT 1 DATE: OCTOBER 29, 2023 Introduction In this report, I analyzed data regarding the distribution of ratings and prices of 4 different wine types. I analyzed differences between average prices and ratings and how those differences should be used towards future selling/production tactics. Overall, I determined that red and sparkling wine have the highest rating and selling price, which could be helpful in production in order to produce the more profit from these 4 wine types. Data Analysis Discussion of Descriptive Statistics • The pair of wines with the largest difference in average prices was rose and sparkling with approximately a 48$ average price difference. Rose and white wine had the smallest average price difference. • The rating skewness for red, white, rose, and sparkling wine are all close to symmetric, falling at -.06, -0.05, 0.09, and 0.12. Red, rose, and white wine price is skewed right, and sparkling wine is closer to symmetric, being slightly skewed right. • There are outliers that are making the price of red wine skewed right, because the maximum is 3410 whereas the average price is the 39 and minimum is far from the average as well. The other wine price skewness is not as affected by any outliers. Discussion of Rating ANOVA • H 0 : The average wine rating is equal across the 4 wine types (Red, White, Rose, Sparkling) • H A : There is at least one difference in average wine rating across the 4 wine types (Red, White, Rose, Sparkling) • Because the p-value of 2.35E-74 is less than our significance level of 0.05, we can reject the null hypothesis and can conclude that there is at least one difference in average wine rating across our 4 wine types. • In the average ratings, Sparkling wine had the highest rating, so I would recommend producing more sparkling wine based off of higher average ratings from across the 4 wine types. Discussion of Price ANOVA • H 0 : The average price rating is equal across the 4 wine types. • H A : There is at least one difference in average wine rating across the 4 wine types • Because the p-value of 1.54E-52 is less than our significance level of 0.05, we can reject the null hypothesis and can conclude that there is at least one difference in average wine prices across the 4 wine types.

• Since Sparkling wine also had the highest average selling price, I would recommend to Gallo to produce more sparkling wine to increase profits from selling more of the most profitable wine. Discussion of Bar Graph • There appears to be a relationship between the highest rating and highest price, for example Rose has the lowest rating and the lowest average price. Additionally, Sparkling wine has the highest rating and the highest selling price. From the bar graph we can conclude that there is a correlation between the average rating of wine type and the average price for the same wine type. • Based off the previous analysis above, there is not any consistency between price and rating across wine types. There are definitive higher rated/priced wines and lower rated/priced wine types with few overlaps in average pricing. • From the bar graph analysis, I would recommend producing more of the wines with higher selling price, from the relationship we discovered between price and rating. Red wine and sparkling wine were both highest rated, and had the highest average selling price, so producing more red and sparkling wine will increase profits. Conclusion In conclusion, there were clear connections between average rating and price from these 4 types of wine. The higher ratings resulted in a higher selling price and vice versa, which can be helpful towards producing more profit by selling more profitable wines, or to increase the production/customer ratings for lower rated wines. One feature of the data to note is the outliers possibly present in the red wine analysis. The average is skewed right and could be from outliers over 80 times the average selling price. This is important to keep in mind when analyzing data, however outliers did not vastly affect the other types of wine. Overall, the inconsistency between average price/average rating across wines could mean it might be more profitable to only produce fewer types of wine, or to increase the quality of lower rated wines. These visual/interpreted analyses are extremely helpful in towards determining a company’s next steps and can provide trends and averages that are not clear from a larger data set.

Figure 3-Rose Descriptive Statistics Figure 4-Sparkling Descriptive Statistics Rating Price Mean 3.742710997 Mean 12.60363171 Standard Error 0.013846617 Standard Error 0.816335258 Median 3.8 Median 8.94 Mode 3.8 Mode 7.9 Standard Deviation 0.273799133 Standard Deviation 16.14198476 Sample Variance 0.074965965 Sample Variance 260.5636719 Kurtosis 0.70994998 Kurtosis 123.9274509 Skewness 0.087687377 Skewness 9.576956589 Range 2.1 Range 245.3 Minimum 2.7 Minimum 3.7 Maximum 4.8 Maximum 249 Sum 1463.4 Sum 4928.02 Count 391 Count 391 Maddy Raker Rating Price Mean 4.065942029 Mean 60.74271739 Standard Error 0.017156128 Standard Error 4.482181869 Median 4.1 Median 35.135 Mode 3.9 Mode 14.9 Standard Deviation 0.285019011 Standard Deviation 74.46359774 Sample Variance 0.081235837 Sample Variance 5544.827388 Kurtosis -0.574942406 Kurtosis 8.88351574 Skewness 0.120145014 Skewness 2.701111008 Range 1.4 Range 488.5 Minimum 3.3 Minimum 6.5 Maximum 4.7 Maximum 495 Sum 1122.2 Sum 16764.99 Count 276 Count 276 Maddy Raker

Figure 5-Rating ANOVA Output Figure 6-Price ANOVA Output Anova: Single Factor SUMMARY Groups Count Sum Average Variance Red Rating 8653 33663.1 3.890338611 0.095052278 White Rating 3754 14335.4 3.818700053 0.070233748 Rose Rating 391 1463.4 3.742710997 0.074965965 Sparkling Rating 276 1122.2 4.065942029 0.081235837 ANOVA Source of Variation SS df MS F P-value F crit Between Groups 30.37892023 3 10.12630674 116.3466346 2.34555E-74 2.605588512 Within Groups 1137.556146 13070 0.087035665 Total 1167.935066 13073 Maddy Raker Anova: Single Factor SUMMARY Groups Count Sum Average Variance Red Price 8653 338895.7 39.16511037 7222.519919 White Price 3754 77484.06 20.64039957 958.1641816 Rose Price 391 4928.02 12.60363171 260.5636719 Sparkling Price 276 16764.99 60.74271739 5544.827388 ANOVA Source of Variation SS df MS F P-value F crit Between Groups 1274071.688 3 424690.5628 81.97560105 1.54506E-52 2.605588512 Within Groups 67711679.88 13070 5180.694711 Total 68985751.56 13073 Maddy Raker