: 9.190 > :7.300   Min. Examples. Format > in thousands of dollars along with the sales (in thousands of units). sales price ad_type price_apple price_cookies A data frame with 392 observations on the following 9 variables. Chapter 1 Linear regression with R. Reading materials: Slides 3 - 11 in STA108_LinearRegression_S20.pdf.. Fitting a linear model is simple in R.The bare minimum requires you to know only two functions lm() and summary().We will apply linear regression on three data set advertising, flu shot, and Project STAR. It contains 1338 rows of data and the following columns: age, gender, BMI, children, smoker, region, insurance charges. From the above summary table, we can roughly know the basic statistics of each numeric variable. [1] 10.03942 :0.0   Min. In reality, we can reasonably set the price to be 10 or 9.99. A data frame containing the impact of three advertising medias (youtube, facebook and newspaper) on sales. However, this is only the conclusion based on the sample with only 30 observations randomly selected. The output is the same as we use the function of “lm” for regression. Marketing Data Set. FBI Crime Data. The data is related with direct marketing campaigns (phone calls) of a Portuguese banking institution. Some of them are listed below. Call: Which type of in-store advertisement is more effective? Shapiro-Wilk normality test F-Statistic: The F-test is statistically significant. Median :204.5 Median : 9.855 Median :0.5  Median :7.580   Median : 9.515 They have placed two types of ads in stores for testing, one theme is natural production of the juice, the other theme is family health caring; The Price Elasticity – the reactions of sales quantity of the grape juice to its price change; The Cross-price Elasticity – the reactions of sales quantity of the grape juice to the price changes of other products such as apple juice and cookies in the same store; How to find the best unit price of the grape juice which can maximize the profit and the forecast of sales with that price. Explore Popular Topics Like Government, Sports, Medicine, Fintech, Food, More. Thanks. Fifa 18 More Complete Player Dataset: An extension of the previous dataset, this version contains several extra fields and is pre-cleaned to a much greater extent. The Adjusted R-square takes in to account the number of variables and so it’s more useful for the multiple regression analysis. Getting Started with R; Understanding your Data Set; Analysing & Building Visualisations; 1. Now we can conduct the t-test since the t-test assumptions are met. The assumptions are met. Price charged by competitor at each location.  -92.92234 -27.07766 Data are the advertising budget in thousands of dollars along with the sales (in thousands of units). # plotting the residuals vs. other key model metrics We can further use the model to predict the sales while the price is 10. business_center . The advertising experiment has been repeated 200 times. FIFA 19 complete player dataset: Detailed attributes for every player registered in the latest edition of the FIFA 19 database scraped from SoFIFA. Please ignore the statistics of the “ad_type” there since it is a categorical variable. > $maximum The marketing campaigns were based on phone calls. 1 215.1978 176.0138 254.3817. The in-store advertisement type to promote the grape juice. data = df), Residuals: football.db: A free and open public domain football database & schema for use in any programming language. W = 0.8974, p-value = 0.08695. acceleration 1. However, according to our real-life experience, we know when apple juice price is lower, consumers likely to buy more apple juice, and then the sales of other fruit juice will decrease. > lines(density(sales_ad_family$sales),lty=”dashed”,lwd=2.5,col=”red”). Here, “sales” is the dependent variable and the others are independent variables. It us uploaded only for learning purposes. :7.438    1st Qu. > sales_ad_family = subset(df,ad_type==1) The classification goal is to predict if the client will subscribe a term deposit . We can calculate the profit (Y) by the following formula. We can investigate the multicollinearity by displaying the correlation coefficients of the independent variables in pairs as what we did at the beginning of this part. :244.2  3rd Qu. 2   201  9.72       1        7.43          9.62 Otherwise the results of t-tests are not valid. The p-values of the Shapiro-Wilk tests are larger than 0.05, so there is no strong evidence to reject the null hypothesis that the two groups of sales data are normally distributed. > # histogram to explore the data distribution shape For more information on customizing the embed code, read Embedding Snippets. Let’s get started. We can further explore the distribution of the data of sales by visualizing the data in graphical form as follows. 1.246084      1.189685      1.149248      1.099255. (Intercept)    774.813    145.349   5.331 1.59e-05 *** “Price elasticity is defined as %ΔQ/%ΔP, which indicates the percent change in quantity divided by the percent change in price; Cross-price Elasticity is the percent change in quantity divided by the change in the price of some other product.”1, PE = (ΔQ/Q) / (ΔP/P) = (ΔQ/ΔP) * (P/Q) = -51.24 * 0.045 = -2.3, ΔQ/ΔP = -51.24 , the parameter before the variable “price” in the above model, P/Q = 9.738 / 216.7 = 0.045,  P is the mean of prices in the dataset, so does Q. t = -3.7515, df = 25.257, p-value = 0.0009233 Example data set: Teens, Social Media & Technology 2018. price          -51.239      5.321  -9.630 6.83e-10 *** There are 5 variables (data columns) in the dataset. Traditionally the analysis tools are mainly SPSS and SAS, however, the open source R language is catching up now. Usage Sales = 774.81 – 51.24 * price + 29.74 * ad_type + 22.1 * price_apple – 25.28 * price_cookies, With model established, we can analysis the Price Elasticity(PE) and Cross-price Elasticity(CPE) to predict the reactions of sales quantity to price. The dataset can be downloaded here. License. 转载须以超链接形式标明文章原始出处和作者信息, Copyright © 2020 | MH Corporate basic by MH Themes, https://blogs.oracle.com/R/entry/analyzing_big_data_using_the, Click here if you're looking to post or find an R/data-science job, PCA vs Autoencoders for Dimensionality Reduction, Video + code from workshop on Deep Learning with Keras and TensorFlow, The First Programming Design Pattern in pxWorks, BASIC XAI with DALEX— Part 1: Introduction, Hack: The “count(case when … else … end)” in dplyr, The Bachelorette Ep. Download Open Datasets on 1000s of Projects + Share Projects on One Platform. Sales. > # histogram to explore the data distribution shapes Let’s investigate the correlation between the sales and other variables by displaying the correlation coefficients in pairs. We don’t find outliers in the above box plot graph and the sales data distribution is roughly normal. 2 stars 3 forks Star Watch Code; Issues 0; Pull requests 0; Actions; Projects 0; Security; Insights; Dismiss Join GitHub today. > sales.reg In this Data Science R Project series, we will perform one of the most essential applications of machine learning – Customer Segmentation. Set ; Analysing & Building visualisations ; 1: the R-squared is very high here because dataset... Company … the machine learnt the little details of the above box plot graph and the are... Each numeric advertising dataset r time period it represents, too sales = 774.81 – 51.24 * price + 29.74 1! ) on sales since they were randomly sampled 30 observations randomly selected: Detailed attributes for every player in. 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