Lab 6. Linear regression
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Shiny app for regression line
shiny::runGitHub("agricolamz/regression_app")
Data
1. First dataset is about language acquisition. In [Huttenlocher, Vasilyeva, Cymerman, Levine 2002] authors analysed 46 pairs of mothers and children (aged from 47 to 59 months, mean age -- 54). They recorded and trinscribed 2 hours from each child day. During the study they collected number of noun phrases per utterance in mother speech to the number of noun phrases per utterance in child speech.
np_acquisition <- read.csv("https://goo.gl/DhpmLe")

2. Second dataset contains relative frequencies (proportions) of the first-person and second-person pronouns in 69 subcorpora of the British National Corpus (the BYU-BNC version).
bnc_pron <- read.csv("https://goo.gl/vt1HCc")
1.1 Visualise acquisition data using geom_smooth(method = "lm") for line.
1.2 Visualise pronouns data using geom_smooth(method = "lm") for line.
2.1 Calculate Pearson correlation coefficient for acquisition dataset
2.2 Calculate Pearson correlation coefficient for pronouns dataset
3.1 Make a linear regression using number of noun phrase in mothers' speech as a predictor. Provide a formula of the regression line.
e. g. child=0.6219+0.2391*mother
3.2 Make a linear regression using number of proportion of the second-person pronouns as a predictor. Provide a formula of the regression line.
e. g. P1=0.008328+0.823748*P2 (without spaces!)
4.1 Compare R² from the acquisition data regression model (q 3.1) with squered correlation coefficient from (q 2.1).
Clear selection
4.2 Compare R² from the acquisition data regression model (q 3.2) with squered correlation coefficient from (q 2.2).
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5.1 Predict, what would be the number of noun phrase in child's speech, if the number of noun phrase in mother's speech is 2.
5.2 Predict, what would be the proportion of the first-person pronouns, in text with the proportion of the second-person pronouns 0.04.
Clear selection
6.1 Analyse  residuals plot for first dataset.
6.2 Analyse residuals plot for the second dataset
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