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(d) The residual for the New York Yankees indicate that there is high error in home attendance prediction for the Yankees. The observed home attendance for the New York Yankees is much higher than predicted value using linear regression model.
(a) There appears a non-linear relationship between Human Development Index (HDI) and the number of cell phone subscribers. The relationship between HDI and the number of cell phone subscribers appears to be logistic (similar to exponential) that is increases quickly at first, and than slows down, and than finally reaches at a saturation point. Therefore, fitting a linear model to these data might be misleading.
(b) The scatterplot of residuals versus predicted HDI will look like a bend (curved) graph. The errors (residuals) will be positive at start (left) and than drop down in the middle and drop down further (negative) on the right.
3) If that point were removed from the data, the correlation would become weaker (or no correlation). Removing this point there appear no relationship between x and y. This point is far from all other scattered points and thus tries to form a linear relationship and thus increases the
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The study chosen was the Williams et. al. (1998) study wherein the researchers attempted to evaluate the extent to which patient literacy was related to their knowledge of asthma and effective use of the
In deed, my grasp of the content taught fascinated me.
Overall, the course was very interesting. I found it motivating using the statistics disc to analyze data. My favorites were the MSL assignments, hypothesis testing, descriptive
lawed findings resulted to unscheduled drop in patients every day and corresponding occurrence of no-shows thus compromising the prevailing list that were mainly based on the patients scheduled on the past days.
Print material is more credible than internet material because
The effect of income is not clear, given that higher income does not guarantee higher life expectancy and vice versa.
In a nutshell, income slightly influences life expectancy, but might not be as a good predictor due to the
The mode, for example, is suitable for describing data on a nominal scale while for ordinal data; either the mode or the median should be used. The mean is however more suitable for variables on a ratio or interval scale (Hanna and