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Author: CodingGenius14

finalproject-oberai.R

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+# A brief explanation of why you chose this data and the source of the data.
+# An explanation of the data set and it's variables.
+# A correlation matrix
+# Scatter plots for all pairwise variables (not dummy variables).
+# A description of what the correlation matrix and scatter plots indicate.
+# A description of the initial model to be tested.
+# An analysis of the overall fit of the model.
+# An analysis of the tests of significance for the coefficients.
+# Interpret each coefficient that is not a dummy variable or interaction term.
+# An explanation of the value of the coefficient of determination.
+# A residual plot for each non-dummy predictor in the model with a description of what is indicated.
+# A boxplot of the residuals and analysis.
+# A QQ-plot of the residuals and analysis.
+# Choose one reasonable combination of your predictor values and calculate and interpret the predictions.
+# A summarizing paragraph describing how well the model fits the data.
+# A written summary of your analysis submitted in an R Markdown file will be worth 100 points.  
+
+
+# packages
+library(ggplot2)
+library(GGally) 
+library(qqplotr)
+
+
+
+# Tesla data set
+# Close.Last is the dependent variable
+# Volume, Open, High, and Low are the predictors
+# Date is excluded from the Data set as the dates are all from the same month and plays no significance
+tesla_stock <- read.csv("Tesla-data.csv")
+
+
+
+# excluding the "data" column and removing the "$" symbol from the "close", "Open", "High" and "Low" column
+tesla_stock_numeric <- tesla_stock[, !names(tesla_stock) %in% "Date"]
+tesla_stock_numeric$Close.Last <- as.numeric(gsub("\\$", "", tesla_stock_numeric$Close.Last))
+tesla_stock_numeric$Volume <- as.numeric(gsub("\\$", "", tesla_stock_numeric$Volume))
+tesla_stock_numeric$Open <- as.numeric(gsub("\\$", "", tesla_stock_numeric$Open))
+tesla_stock_numeric$High <- as.numeric(gsub("\\$", "", tesla_stock_numeric$High))
+tesla_stock_numeric$Low <- as.numeric(gsub("\\$", "", tesla_stock_numeric$Low))
+
+
+
+# correlation matrix
+cor_matrix <- cor(tesla_stock_numeric)
+print(cor_matrix)
+
+
+
+# scatterplot for all pairwise variables
+ggpairs(data = tesla_stock_numeric,
+        columnLabels = c("Close/Last", "Volume", "Open", "High", "Low"))
+
+
+
+# regression model
+regression_model <- lm(data = tesla_stock_numeric, formula = Close.Last ~ Volume + Open + High + Low)
+summary(regression_model)
+anova(regression_model)
+
+
+
+# coefficients
+regression_model$coefficients
+
+
+
+# adjusted R Squared Value
+summary(regression_model)$adj.r.squared
+
+
+
+
+ggplot(tesla_stock_numeric, aes(x = Volume, y = regression_model$residuals)) +
+  geom_point() +
+  geom_hline(yintercept = 0, color = "blue")
+
+ggplot(tesla_stock_numeric, aes(x = Open, y = regression_model$residuals)) +
+  geom_point() +
+  geom_hline(yintercept = 0, color = "blue")
+
+ggplot(tesla_stock_numeric, aes(x = High, y = regression_model$residuals)) +
+  geom_point() +
+  geom_hline(yintercept = 0, color = "blue")
+
+ggplot(tesla_stock_numeric, aes(x = Low, y = regression_model$residuals)) +
+  geom_point() +
+  geom_hline(yintercept = 0, color = "blue")
+
+
+
+# Residual Boxplot
+residuals <- data.frame(residual = regression_model$residuals)
+
+ggplot(residuals, aes(x = residual)) +
+  geom_boxplot()
+
+
+
+# qqplot for the normality of the residuals
+ggplot(residuals, aes(sample = residual)) +
+  stat_qq_point() +
+  stat_qq_line() +
+  stat_qq_band()
+
+
+
+
+# predictions
+
+new_data <- data.frame(
+  Volume = c(125000000),
+  Open = c(175),
+  High = c(182),
+  Low = c(171)
+)
+
+predicted_closing_price <- predict(regression_model, newdata = new_data, interval = "confidence")
+
+predicted_closing_price
+
+