### [RStudio/Graduate] Need held developing a table in RStudio

update:

title: "Spring 2016 PSY737 Final" author: "Keegan Talty" date: "December 14, 2016" output: html_document

'''{r} ''' '''{r} data <- data.frame(state.x77) ```

# Mardown:

state.x77: matrix with 50 rows and 8 columns giving the following statistics in the respective columns.

## Population:

population estimate as of July 1, 1975

## Income:

per capita income (1974)

## Illiteracy:

illiteracy (1970, percent of population)

## Life Exp:

life expectancy in years (1969–71)

## Murder:

murder and non-negligent manslaughter rate per 100,000 population (1976)

## Frost:

mean number of days with minimum temperature below freezing (1931–1960) in capital or large city

## Area:

land area in square miles

### Source

U.S. Department of Commerce, Bureau of the Census (1977) Statistical Abstract of the United States.

U.S. Department of Commerce, Bureau of the Census (1977) County and City Data Book.

### References

Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) The New S Language. Wadsworth & Brooks/Cole.

Variable| Variable Description

```{r} attach(data) summary(data) ```

```{r fig.height = 12, fig.width=6} dotchart(Life.Exp, row.names(data))) ``` a. Which state had the highest ife expectancy? b. Which state had the lowest life expectancy?

```{r} scatterplot matrix; plot(st) ``` a) Which pairs of variables appear to have a linear relationship with Life Expectancy? b) Which do not?

```{r} cor(data) ``` a) Which variables correlate most strongly with Life Expectancy? b) Which least strongly?

```{r} cor.test(Life.Exp, Frost) ``` What can you conclude about relatioship btwn Life.Exp and Frost? p < .05 standard

```{r} Object1<-lm(Life.Exp ~ Murder + HS.Grad + Frost) summary(Object1) anova(Object1) ``` d) What is the multiple R-square for the regression model? e) Which variables contribute to the prediction of Life.Exp ata statistically significant level? (p <.05)

```{r} plot(Murder,Life.Exp) ```

```{r} points{Georgia; pch = 16; col=red} text("Georgia") lines(Murder, (72.9736 + (-.2839 * Murder))) ```

```{r} model2<-lm(Life.Exp ~ Murder) model2 ``` e) Would you describe the relationship between Life.Exp and Murder as strong or weak? f) Positive of Negative?