This is completed in R

### Allowable packages

the only allowable packages are `tidyverse`, `psych` and `lm.beta`.

### Data Set

- These data come from the [Centers for Disease Control and Prevention](www.cdc.gov)

### Q8: Generate a regression predicting general health based on both number of fruits consumed per day and the length of time since last checkup (in that order). Analyze the summary of the model. How would you assess this model? Store the summary of the standardized regression coefficients in `Q8.`

Hint: The resulting summary output should look like this:

```

Call:

[...]

Residuals:

Min 1Q Median 3Q Max

Coefficients:

Estimate Standardized Std. Error t value Pr(>|t|)

(Intercept) 2.672440

FRTDAY

[...]2

[...]3

[...]4

[...]8

---

Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: on degrees of freedom

Multiple R-squared: , Adjusted R-squared:

F-statistic: on and DF, p-value:

### Q9: Generate two new columns in `brf_part1` with binary data. For any health level of very good or excellent, set the value to 1; otherwise, a 0. Call this `binHealth`. For any person attending a checkup within the past year, set to a value of 1; otherwise, 0. Call this `binCheckup`. With the updated dataframe, perform a logistic regression to predict the likelihood of an individual's general health being very good or excellent based on `FRTDAY` and `binCheckup.` Store the summary of the regression coefficients in `Q9.`

Hint: The resulting summary output should look like this:

```

Call:

glm(...)

Coefficients:

Estimate Std. Error z value Pr(>|z|)

(Intercept) 0.175875

FRTDAY

binCheckup1

---

Signif. codes:

0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

Null deviance: on 420719 degrees of freedom

Residual deviance: on 420717 degrees of freedom

AIC:

Number of Fisher Scoring iterations:

### Q10: Generate a dataframe with data on five new individuals, shown in the table below. Then use your logistic regression model above and this new data to predict the likelihood of each individual would respond that they are in very good or excellent health. Use the `predict()` function. Round predictions to 2 decimal places. Store the predictions in `Q10`.

| ID | FRTDAY | binCheckup |

|---------|--------|------------|

| Person1 | 0 | 0 |

| Person2 | 1 | 0 |

| Person3 | 2 | 0 |

| Person4 | 3 | 1 |

| Person5 | 6 | 1 |

Hint 1: As a binary variable, binCheckup should *not* be coded as numeric.

Hint 2: The prediction output should look like this:

```

1 2 3 4 5

0.18

```

Label: Length of time since last routine checkup Section Name: Health Care Access Core Section Number: 3 Question Number: 4 Column: 112 Type of Variable: Num SAS Variable Name: CHECKUP1 Question Prologue: Question: About how long has it been since you last visited a doctor for a routine checkup? [A routine checkup is a general physical exam, not an exam for a specific injury, illness, or condition.] Weighted Value Value Label Frequency Percentage Percentage 1 Within past year (anytime