Modeling Guidance

Model Guidance

Kelly McConville

Stat 100
Week 8 | Fall 2023

term	estimate	std_error	statistic	p_value	lower_ci	upper_ci
intercept	20.917	10.851	1.928	0.055	-0.434	42.267
poly(AudienceScore, degree = 2, raw = TRUE)1	-0.259	0.377	-0.687	0.492	-1.000	0.482
poly(AudienceScore, degree = 2, raw = TRUE)2	0.010	0.003	3.284	0.001	0.004	0.016
Genre: Drama	5.867	2.420	2.424	0.016	1.105	10.630
Genre: Horror	2.237	3.047	0.734	0.463	-3.758	8.233

term	estimate	std_error	statistic	p_value	lower_ci	upper_ci
intercept	-12.472	4.083	-3.055	0.002	-20.506	-4.439
AudienceScore	0.975	0.072	13.590	0.000	0.834	1.117
DomesticGross	-0.006	0.015	-0.431	0.667	-0.035	0.023
Genre: Drama	6.117	2.644	2.314	0.021	0.916	11.319
Genre: Horror	2.058	3.141	0.655	0.513	-4.121	8.238

library(tidyverse)
ce <- read_csv("data/fmli.csv")
summarize(ce, meanFINCBTAX = mean(FINCBTAX))

# A tibble: 1 × 1
  meanFINCBTAX
         <dbl>
1       62480.

Now we need to distinguish between the population and the sample

Parameters:
- Based on the population
- Unknown then if don’t have data on the whole population
- EX: $β_{o}$ and $β_{1}$
- EX: $μ$ = population mean

Statistics:
- Based on the sample data
- Known
- Usually estimate a population parameter
- EX: ${\hat{β}}_{o}$ and ${\hat{β}}_{1}$
- EX: $\bar{x}$ = sample mean

Model Guidance Kelly McConville Stat 100 Week 8 | Fall 2023