class: center, middle, inverse, title-slide # lesson 08 --- --- class: inverse, left, bottom background-image: url(https://images.unsplash.com/photo-1501641466388-c67e34ec767e?ixid=MXwxMjA3fDB8MHxzZWFyY2h8Mnx8b2xkJTIwZmFpdGhmdWx8ZW58MHx8MHw%3D&ixlib=rb-1.2.1&auto=format&fit=crop&w=500&q=60) background-size: cover # .Large[] ## .small[] #### .tiny[Dr. Evangeline Reynolds | Meeting 4 | 2021-02-02 | Image credit: Emily Campbell, Upsplash] --- count: false .panel1-observed_statistic-auto[ ```r *library(tidyverse) ``` ] .panel2-observed_statistic-auto[ ] --- count: false .panel1-observed_statistic-auto[ ```r library(tidyverse) *ma206data::chap2_SnippetEstimate ``` ] .panel2-observed_statistic-auto[ ``` ## # A tibble: 48 × 1 ## estimate ## <dbl> ## 1 10 ## 2 12 ## 3 6 ## 4 13 ## 5 15 ## 6 10 ## 7 15 ## 8 21 ## 9 10 ## 10 15 ## # … with 38 more rows ``` ] --- count: false .panel1-observed_statistic-auto[ ```r library(tidyverse) ma206data::chap2_SnippetEstimate -> * Elapsed.Time ``` ] .panel2-observed_statistic-auto[ ] --- count: false .panel1-observed_statistic-auto[ ```r library(tidyverse) ma206data::chap2_SnippetEstimate -> Elapsed.Time *ggplot(data = Elapsed.Time) ``` ] .panel2-observed_statistic-auto[ <!-- --> ] --- count: false .panel1-observed_statistic-auto[ ```r library(tidyverse) ma206data::chap2_SnippetEstimate -> Elapsed.Time ggplot(data = Elapsed.Time) + * aes(x = estimate) ``` ] .panel2-observed_statistic-auto[ <!-- --> ] --- count: false .panel1-observed_statistic-auto[ ```r library(tidyverse) ma206data::chap2_SnippetEstimate -> Elapsed.Time ggplot(data = Elapsed.Time) + aes(x = estimate) + * geom_dotplot() ``` ] .panel2-observed_statistic-auto[ <!-- --> ] --- count: false .panel1-observed_statistic-auto[ ```r library(tidyverse) ma206data::chap2_SnippetEstimate -> Elapsed.Time ggplot(data = Elapsed.Time) + aes(x = estimate) + geom_dotplot() + * ggxmean::geom_x_mean() ``` ] .panel2-observed_statistic-auto[ <!-- --> ] --- count: false .panel1-observed_statistic-auto[ ```r library(tidyverse) ma206data::chap2_SnippetEstimate -> Elapsed.Time ggplot(data = Elapsed.Time) + aes(x = estimate) + geom_dotplot() + ggxmean::geom_x_mean() + * ggxmean::geom_x_mean_label() ``` ] .panel2-observed_statistic-auto[ <!-- --> ] --- count: false .panel1-observed_statistic-auto[ ```r library(tidyverse) ma206data::chap2_SnippetEstimate -> Elapsed.Time ggplot(data = Elapsed.Time) + aes(x = estimate) + geom_dotplot() + ggxmean::geom_x_mean() + ggxmean::geom_x_mean_label() + * ggxmean::geom_x_median(linetype = "dashed") ``` ] .panel2-observed_statistic-auto[ <!-- --> ] --- count: false .panel1-observed_statistic-auto[ ```r library(tidyverse) ma206data::chap2_SnippetEstimate -> Elapsed.Time ggplot(data = Elapsed.Time) + aes(x = estimate) + geom_dotplot() + ggxmean::geom_x_mean() + ggxmean::geom_x_mean_label() + ggxmean::geom_x_median(linetype = "dashed") ``` ] .panel2-observed_statistic-auto[ <!-- --> ] <style> .panel1-observed_statistic-auto { color: black; width: 38.6060606060606%; hight: 32%; float: left; padding-left: 1%; font-size: 80% } .panel2-observed_statistic-auto { color: black; width: 59.3939393939394%; hight: 32%; float: left; padding-left: 1%; font-size: 80% } .panel3-observed_statistic-auto { color: black; width: NA%; hight: 33%; float: left; padding-left: 1%; font-size: 80% } </style> --- count: false .panel1-calcs-auto[ ```r *Elapsed.Time ``` ] .panel2-calcs-auto[ ``` ## # A tibble: 48 × 1 ## estimate ## <dbl> ## 1 10 ## 2 12 ## 3 6 ## 4 13 ## 5 15 ## 6 10 ## 7 15 ## 8 21 ## 9 10 ## 10 15 ## # … with 38 more rows ``` ] --- count: false .panel1-calcs-auto[ ```r Elapsed.Time %>% * summarise( * mean = mean(estimate), * s = sd(estimate), * n = n() * ) ``` ] .panel2-calcs-auto[ ``` ## # A tibble: 1 × 3 ## mean s n ## <dbl> <dbl> <int> ## 1 13.7 6.50 48 ``` ] --- count: false .panel1-calcs-auto[ ```r Elapsed.Time %>% summarise( mean = mean(estimate), s = sd(estimate), n = n() ) *xbar = 13.7 ``` ] .panel2-calcs-auto[ ``` ## # A tibble: 1 × 3 ## mean s n ## <dbl> <dbl> <int> ## 1 13.7 6.50 48 ``` ] --- count: false .panel1-calcs-auto[ ```r Elapsed.Time %>% summarise( mean = mean(estimate), s = sd(estimate), n = n() ) xbar = 13.7 *s = 6.5 ``` ] .panel2-calcs-auto[ ``` ## # A tibble: 1 × 3 ## mean s n ## <dbl> <dbl> <int> ## 1 13.7 6.50 48 ``` ] --- count: false .panel1-calcs-auto[ ```r Elapsed.Time %>% summarise( mean = mean(estimate), s = sd(estimate), n = n() ) xbar = 13.7 s = 6.5 *n = 48 ``` ] .panel2-calcs-auto[ ``` ## # A tibble: 1 × 3 ## mean s n ## <dbl> <dbl> <int> ## 1 13.7 6.50 48 ``` ] --- count: false .panel1-calcs-auto[ ```r Elapsed.Time %>% summarise( mean = mean(estimate), s = sd(estimate), n = n() ) xbar = 13.7 s = 6.5 n = 48 *sd = s/sqrt(n) ``` ] .panel2-calcs-auto[ ``` ## # A tibble: 1 × 3 ## mean s n ## <dbl> <dbl> <int> ## 1 13.7 6.50 48 ``` ] --- count: false .panel1-calcs-auto[ ```r Elapsed.Time %>% summarise( mean = mean(estimate), s = sd(estimate), n = n() ) xbar = 13.7 s = 6.5 n = 48 sd = s/sqrt(n) *null = 10 ``` ] .panel2-calcs-auto[ ``` ## # A tibble: 1 × 3 ## mean s n ## <dbl> <dbl> <int> ## 1 13.7 6.50 48 ``` ] --- count: false .panel1-calcs-auto[ ```r Elapsed.Time %>% summarise( mean = mean(estimate), s = sd(estimate), n = n() ) xbar = 13.7 s = 6.5 n = 48 sd = s/sqrt(n) null = 10 *t = (xbar-null)/sd ``` ] .panel2-calcs-auto[ ``` ## # A tibble: 1 × 3 ## mean s n ## <dbl> <dbl> <int> ## 1 13.7 6.50 48 ``` ] --- count: false .panel1-calcs-auto[ ```r Elapsed.Time %>% summarise( mean = mean(estimate), s = sd(estimate), n = n() ) xbar = 13.7 s = 6.5 n = 48 sd = s/sqrt(n) null = 10 t = (xbar-null)/sd *t ``` ] .panel2-calcs-auto[ ``` ## # A tibble: 1 × 3 ## mean s n ## <dbl> <dbl> <int> ## 1 13.7 6.50 48 ``` ``` ## [1] 3.943746 ``` ] --- count: false .panel1-calcs-auto[ ```r Elapsed.Time %>% summarise( mean = mean(estimate), s = sd(estimate), n = n() ) xbar = 13.7 s = 6.5 n = 48 sd = s/sqrt(n) null = 10 t = (xbar-null)/sd t *pvalue = 2*(1-pt(abs(t), n-1)) ``` ] .panel2-calcs-auto[ ``` ## # A tibble: 1 × 3 ## mean s n ## <dbl> <dbl> <int> ## 1 13.7 6.50 48 ``` ``` ## [1] 3.943746 ``` ] --- count: false .panel1-calcs-auto[ ```r Elapsed.Time %>% summarise( mean = mean(estimate), s = sd(estimate), n = n() ) xbar = 13.7 s = 6.5 n = 48 sd = s/sqrt(n) null = 10 t = (xbar-null)/sd t pvalue = 2*(1-pt(abs(t), n-1)) *pvalue ``` ] .panel2-calcs-auto[ ``` ## # A tibble: 1 × 3 ## mean s n ## <dbl> <dbl> <int> ## 1 13.7 6.50 48 ``` ``` ## [1] 3.943746 ``` ``` ## [1] 0.0002658216 ``` ] --- count: false .panel1-calcs-auto[ ```r Elapsed.Time %>% summarise( mean = mean(estimate), s = sd(estimate), n = n() ) xbar = 13.7 s = 6.5 n = 48 sd = s/sqrt(n) null = 10 t = (xbar-null)/sd t pvalue = 2*(1-pt(abs(t), n-1)) pvalue ``` ] .panel2-calcs-auto[ ``` ## # A tibble: 1 × 3 ## mean s n ## <dbl> <dbl> <int> ## 1 13.7 6.50 48 ``` ``` ## [1] 3.943746 ``` ``` ## [1] 0.0002658216 ``` ] <style> .panel1-calcs-auto { color: black; width: 38.6060606060606%; hight: 32%; float: left; padding-left: 1%; font-size: 80% } .panel2-calcs-auto { color: black; width: 59.3939393939394%; hight: 32%; float: left; padding-left: 1%; font-size: 80% } .panel3-calcs-auto { color: black; width: NA%; hight: 33%; float: left; padding-left: 1%; font-size: 80% } </style> <style type="text/css"> .remark-code{line-height: 1.5; font-size: 120%} @media print { .has-continuation { display: block; } } code.r.hljs.remark-code{ position: relative; overflow-x: hidden; } code.r.hljs.remark-code:hover{ overflow-x:visible; width: 500px; border-style: solid; } </style>