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Must read:Is there a real future in data analysis for self-learners without a math degree?

Inference for Multiple Linear Regression

If you see an error in the article, please comment or drop me an email. Inference for Multiple Linear Regression #Load the data cognitive <- read.csv("http://bit.ly/dasi_cognitive") Let us start with the full model, thus including all variables: #Fit the full model and show the summary cog_full <- lm(kid_score ~ mom_hs + mom_iq + mom_work +

Linear Regression Intro

If you see an error in the article, please comment or drop me an email. The basics of linear regression Linear regression is one form of regression among others. It is probably the most intuitive and easiest one. The reason for regression is to 1) predict values for which there are no observed values and

Comparing Categorical Variables

If you see an error in the article, please comment or drop me an email. Introduction When Do We Test for Goodness of Fit (GOF)? A goodness-of-fit test is a one variable Chi-square test. The goal of a Chi-square goodness-of-fit test is to determine whether a set of frequencies or proportions is similar to and

Analysis of Variance (ANOVA)

If you see an error in the article, please comment or drop me an email. Three Conditions for using ANOVA Homogeneity of variances in each group sd_1 <- 64.43 sd_2 <- 38.63 sd_3 <- 52.24 sd_4 <- 64.90 sd_5 <- 54.13 sd_6 <- 48.84 sds <- c(sd_1,sd_2,sd_3,sd_4,sd_5,sd_6) sds_ratio <- round(min(sds)/max(sds),2) print(sds_ratio) ## [1] 0.6 ifelse(sds_ratio>=.5

Proportions

If you see an error in the article, please comment or drop me an email. Conditions for near normality of the distribution of sample proportions? 1 observations are independent 2 sample size: np >= 10 and n (1 – p) >= 10 Proportion inference in a nutshell Let’s say we are interested in the proportion

Bootstrapping

If you see an error in the article, please comment or drop me an email. The basic bootstrap principle uses observed data to construct an estimated population distribution using random sampling with replacement. Sample –> samplings –> estimated distribution Steps of bootstrapping 1 Take a bootstrap sample (random sample with replacement, of the same size

Distributions and probability

If you see an error in the article, please comment or drop me an email. Normal distribution Randomized sample of independent and identically distributed variables. Normal model can be used for sampling distributions if sample size > 30 independent probabilities randomized F distribution Ratio of the mean squares of n1 and n2 independent standard normals.

Foundations for inference

If you see an error in the article, please comment or drop me an email. Getting confused with the basics of inference? If you are a newbie to statistics, or just have not dealt with such kind of topic for a while, you might get confused by the many new terms, some of which sound

Power and sample size

If you see an error in the article, please comment or drop me an email. You can calculate the required sample size for a targeted level of power… …or the obtained power with a given sample size. Calculate the obtained power with a given sample size 1) Hypotheses H_0 : mu_diff = 0 H_A :

Inferential Statistics

Here is a set of flashcards based on the introductory course on Inferential Statistics presented by Brian Caffo of John Hopkins University. See the flashcards on Studyblue… First deck Second deck …or get them in CSV and RDA format here on GitHub