## [1] "C:/Users/nehet/Downloads/old dell/Downloads/analytics classes/term 6/ANLY 545"
data <- read_csv("data.3.csv")
## Rows: 29034 Columns: 8
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## dbl (8): roll 1, roll 2, roll 3, roll 4, roll 5, roll 6, roll 7, roll 8
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
#Analysis (Fact)
#Perform a goodness-of-fit test.
#1. The test’s statistic has a value of χ2 = _14.413_____.
#2. The test has a p-value of _0.0443_____.
#3. The test provides evidence that the distribution [is]/[is not] __is not binomial___ because _p value is less than 0.05, so we reject the null hypothesis_____.
#Find the maximum dice size: 10
## [1] 10
#Find the group size: 8
## [1] 8
#Considering a roll of 1/2/3 to be a success, find the number of successes per grouping.
## 0 1 2 3 4 5 6 7 8 9 10 11 12 13
## Success 1681 5718 8495 7531 4017 1301 251 39 1 0 0 0 0 0
#Number of successes when rolling a 10 sided dice in groups of 8.
##
## Observed and fitted values for binomial distribution
## with parameters estimated by `ML'
##
## count observed fitted pearson residual
## 0 1681 1675.44957 0.1356003
## 1 5718 5741.97282 -0.3163651
## 2 8495 8609.32218 -1.2321002
## 3 7531 7376.30287 1.8012040
## 4 4017 3949.92216 1.0672958
## 5 1301 1353.68716 -1.4320085
## 6 251 289.95333 -2.2876022
## 7 39 35.48950 0.5892770
## 8 1 1.90042 -0.6531612
## $prob
## [1] 0.2999113
##
## $size
## [1] 8
##
## Goodness-of-fit test for binomial distribution
##
## X^2 df P(> X^2)
## Likelihood Ratio 14.41346 7 0.0442977
