Publications by Aayush Sethi
Case Problem 2: NASCAR predictions
Summary Statistics 1) Predict winnings (Dependent Variable) – Indipendent Variables: Poles, Wins, Top5, top10 2) Indivisual significance: 3) Regression Using dummys for top 2-5 & top 6-10 4) From the regression output in part 3, at a 0.05 level of significance, the relationship of top 2-5 and top 6-10 seeem to have an significant relationship (...
1598 sym R (3915 sym/8 pcs)
Case Problem 3: Car Value
Summary Statistics 1) Estimated regression; DV: Cost/mile – IV: family sedan & upscale sedan 2) Regression 2: DV: Value Score – IV: cost/mile, road-test score, predicted reliability, family and upscale sedan 3) Stepwise variable elemination 4) Do smaller cars better values than larger cars? 5) Predicting Value Score given Road test score 6) P...
1221 sym R (6836 sym/14 pcs)
Business_Forecasting_Session_1
Introduction to R Why Learn R? Resources 1. Getting Started Saving your work Loading Packages Getting Help Basic Mathematical Operations Creating Objects / Basic Functions 2. Data Objects Vectors Lists 3. Data Managment Case Study: Titanic Dataset Inspecting Data Missing Values Subsetting by rows subsetting by columns Sorting by Columns C...
6550 sym R (15496 sym/101 pcs) 5 img
Data_Forecasting_Problem_Set_1
Question 1 Part A: Trend Part B & C: Holt Winter’s Additive Seasonality Part D: Moving Average Question 2 Part A: Trend Part B: Exponential Smoothing Part C: Holt’s Exponential Smoothing Part D: Double Moving Average Question 3 Business Forecasting: Problem Set 1 Aayush Sethi 2021-10-03 Question 1 The data set for question 1 is usin...
1758 sym R (8551 sym/37 pcs) 14 img
MODELS_OPIOIDS
Model 1 – Fixed effects model Model 2 – Fixed effects with a lag variable for overdoses Model 3 – Fixed effects with log of overdose Comparing fixed effects model Model 4: Random Effects Model with lag Model 5: Random Effects Model Comparing Random effects model Opioid Crisis in the US: A State-Level Analysis Using Panel df Aayush 2...
902 sym R (23198 sym/21 pcs) 2 img
Session 2: Dataset Management
Introduction to R Why Learn R? Resources 1. Getting Started Saving your work Loading Packages Getting Help Basic Mathematical Operations Creating Objects / Basic Functions 2. Data Managment Case Study: Titanic Dataset Inspecting Data Missing Values Subsetting by rows subsetting by columns Sorting by Columns Create new variables 3. Visualiz...
6031 sym R (14800 sym/93 pcs) 5 img
Opioids_UDM
1. df Overview 1.1 Missing values 1.2 Summary Statistics 1.3 Normalizing overdose deaths 2 EDA 2.1 overdoses by year 2.2 prescription rates by year 2.3 Overdoses by State 2.4:Boxplots to find df outliers – What do do with this information? 2.5: Scatter plots to analyze predictors. 3. Modeling 3.1 Fixed effects model | Within model as it on...
1603 sym R (14612 sym/26 pcs) 4 img
Business_Forecasting_1
Naive Forecasting Exponential Smoothing Holt’s Trend Method ARIMA Modeling ARIMA Results Write-up: 1 – Basic Forecasting Methods Aayush Sethi 2021-09-25 Naive Forecasting BJ Sales Data naive_data <- data.frame(BJsales); head(naive_data) ## BJsales ## 1 200.1 ## 2 199.5 ## 3 199.4 ## 4 198.9 ## 5 199.0 ## 6 200.2 Forecast ...
1229 sym R (7350 sym/25 pcs) 7 img
Test_1_bus_forecasting
1. Plot plot(DATE, data$inflation, type = 'l') 2. Moving Average data$MA4 <- TTR::SMA(data$inflation, n=4) head(data, n=30) ## # A tibble: 30 x 3 ## DATE inflation MA4 ## <date> <dbl> <dbl> ## 1 1960-01-01 1.46 NA ## 2 1961-01-01 1.07 NA ## 3 1962-01-01 1.20 NA ## 4 1963-01-01 1.24 1.24 ##...
455 sym R (2600 sym/12 pcs) 4 img