SMHS EDA - Revision history

Dinov: /* Loading HTML data in R */

2017-02-09T17:12:15Z

‎Loading HTML data in R

Dinov: /* Loading HTML data in R */

2017-02-09T17:12:02Z

‎Loading HTML data in R

Dinov: /* Loading HTML data in R */

2017-02-09T17:04:56Z

‎Loading HTML data in R

Dinov at 17:00, 9 February 2017

2017-02-09T17:00:03Z

Glenbrau: /* Dot Chart */

2015-03-19T13:53:09Z

‎Dot Chart

Glenbrau: /* QQ Plot */

2015-03-19T13:49:26Z

‎QQ Plot

Glenbrau: /* QQ Plot */

2015-03-19T13:48:26Z

‎QQ Plot

Glenbrau: /* Problems */

2015-03-19T13:48:02Z

‎Problems

Glenbrau: /* Problems */

2015-03-19T13:46:10Z

‎Problems

Glenbrau: /* Mixed quantitative and qualitative methods */

2015-03-19T13:35:48Z

‎Mixed quantitative and qualitative methods

@@ Line 86: / Line 86: @@
 lbls <- paste(lbls,"%",sep="")
 pie(letter$English, labels = lbls, col=rainbow(length(lbls)), main="Pie Chart of English Letter Frequencies")
 </pre>

@@ Line 78: / Line 78: @@
 letter<- html_table(html_nodes(wiki_url,"table")[[1]])
 summary(letter)
 </pre>

@@ Line 72: / Line 72: @@
 Let's demonstrate loading HTML data in R use the [[SOCR_LetterFrequencyData|SOCR Letter Frequency Data]] to and pie charts.
-<code>
+<pre>
 library(rvest)
-wiki_url <- read_html("http://wiki.socr.umich.edu/index.php/SOCR_LetterFrequencyData");
+wiki_url <- read_html("http://wiki.socr.umich.edu/index.php/SOCR_LetterFrequencyData")
-html_nodes(wiki_url, "#content");
+html_nodes(wiki_url, "#content")
-letter<- html_table(html_nodes(wiki_url,"table")[[1]]);
+letter<- html_table(html_nodes(wiki_url,"table")[[1]])
 summary(letter)
-</code>
+</pre>
 ===Applications===

@@ Line 68: / Line 68: @@
 [http://en.wikipedia.org/wiki/Trimean Trimean] is a measure of a probability distribution’s location, which is defined as a weighted average of the distribution’s median and its two quartiles. It combines the median’s emphasis on center values with the midhinge’s attention to the extremes. It is a remarkably efficient estimator of population mean, especially for large data sets (consisting of more than 100 points, for example) from a symmetric population.
 <center> \( \frac{Q_1+2Q_2+Q_3}{4} \)  </center>
 ===Applications===

@@ Line 36: / Line 36: @@
 * Comment: When comparing the two series from the histogram above, we can easily tell that the pattern of Series 2 is more obvious compared to that of Series 1. Our intuition may come from the fact that Series 1 has more extreme values across five days, with the values for Jan 1st and Jan 3rd being extremely high (almost 55 for Jan 1st), while that for Jan 4th is almost -12. However, the values for Series 2 are all above 0 and fluctuate between 5 and 20.
-====[[SOCR_EduMaterials_Activities_Dot_Charts|Dot Chart]]====
+====[[SOCR_EduMaterials_Activities_DotChart|Dot Chart]]====
 <center>[[Image:UMHS Gallaway 07012014 Fig3.PNG|500px]]</center>

@@ Line 56: / Line 56: @@
 ====[[SOCR_EduMaterials_Activities_QQChart|QQ Plot]]====
-In Quantile-Quantile plots, the observed values are plotted against theoretical quantiles in QQ charts. A line of good fit is drawn to show the behavior of the data values against the theoretical distribution. If F() is a cumulative distribution function, then a quantile (q) - which is also known as a percentile - is defined as a solution to the equation \(F(q)=p\),that is \(q=F^{-1}(p)\).
+In Quantile-Quantile plots, the observed values are plotted against theoretical quantiles in QQ charts. A line of good fit is drawn to show the behavior of the data values against the theoretical distribution. If F() is a cumulative distribution function, then a quantile (q) - which is also known as a percentile - is defined as a solution to the equation \(F(q)=p\), that is \(q=F^{-1}(p)\).
 <center>[[Image:UMHS Gallaway 07012014 Fig5.PNG|500px]]</center>

@@ Line 114: / Line 114: @@
 * Which of the following plots would be most useful in comparing the two sets of backpack weights? Choose One Answer:
 : (a) Histograms
-: (b) Dot Plots
+: (b) Dot plots
-: (c) Scatter Plots
+: (c) Scatter plots
-: (d) Box Plots
+: (d) Box plots
 * School administrators are interested in examining the relationship between height and GPA. What type of plot should they use to display this relationship? Choose one answer.
-: (a) Box Plot
+: (a) Box plot
-: (b) Scatter Plot
+: (b) Scatter plot
-: (c) Line Plot
+: (c) Line plot
-: (d) Dot Plot
+: (d) Dot plot
 * What would be the most appropriate plot for comparing the heights of the 8th graders from different ethnic backgrounds? Choose one answer.
-: (a) Bar Charts
+: (a) Bar charts
-: (b) Side by Side Boxplot
+: (b) Side by side box plots
 : (c) Histograms
-: (d) Pie Charts
+: (d) Pie charts
 * There is a company in which a very small minority of males (3%) receives three times the median salary of all males, and a very small minority of females (3%) receives one-third of the median salary of all females. What do you expect the side-by-side box plot of male and female salaries to look like? Choose one answer.

@@ Line 112: / Line 112: @@
 </center>
-* Which of the following plots would be the most useful in comparing the two sets of backpack weights? Choose One Answer:
+* Which of the following plots would be most useful in comparing the two sets of backpack weights? Choose One Answer:
 : (a) Histograms
 : (b) Dot Plots
@@ Line 119: / Line 119: @@
 * School administrators are interested in examining the relationship between height and GPA. What type of plot should they use to display this relationship? Choose one answer.
-: (a) box plot
+: (a) Box Plot
-: (b) scatter plot
+: (b) Scatter Plot
-: (c) line plot
+: (c) Line Plot
-: (d) dot plot
+: (d) Dot Plot
 * What would be the most appropriate plot for comparing the heights of the 8th graders from different ethnic backgrounds? Choose one answer.
-: (a) bar charts
+: (a) Bar Charts
-: (b) side by side boxplot
+: (b) Side by Side Boxplot
-: (c) histograms
+: (c) Histograms
-: (d) pie charts
+: (d) Pie Charts
-* There is a company in which a very small minority of males (3%) receives three times the median salary of all males, and a very small minority of females (3%) receives one-third of the median salary of all females. What do you expect the side-by-side boxplot of male and female salaries to look like? Choose one answer.
+* There is a company in which a very small minority of males (3%) receives three times the median salary of all males, and a very small minority of females (3%) receives one-third of the median salary of all females. What do you expect the side-by-side box plot of male and female salaries to look like? Choose one answer.
-: (a) Both boxplots will be skewed and the median line will not be in the middle of any of the boxes.
+: (a) Both box plots will be skewed and the median line will not be in the middle of any of the boxes.
-: (b) Both boxplots will be skewed, in the case of the females the median line will be close to the top of the box and in the case of the males the median line will be closer to the bottom of the box.
+: (b) Both box plots will be skewed, in the case of the females the median line will be close to the top of the box and in the case of the males the median line will be closer to the bottom of the box.
-: (c) Need to have the actual data to compare the shape of the boxplots.
+: (c) Need to have the actual data to compare the shape of the box plots.
-: (d) Both boxplots will be skewed, in the case of males the median line will be close to the top of the box and for the females the median line will be closer to the bottom of the box.
+: (d) Both box plots will be skewed, in the case of males the median line will be close to the top of the box and for the females the median line will be closer to the bottom of the box.
-* A researcher has collected the following information on a random sample of 200 adults in the 40-50 age range: Weight in pounds Heart beats per minute Smoker or non-smoker Single or married
+* A researcher has collected the following information on a random sample of 200 adults in the 40-50 age range: ''Weight in Pounds'', ''Heart Beats Per Minute'', ''Smoker or Non-Smoker'', and ''Single or Married''. He wants to examine the relationship between: 1) heart beat per minute and weight, and 2) smoking and marital status. Choose one answer.
 : (a) He should draw a scatter plot of heart beat and weight, and a segmented bar chart of smoking and marital status.
-: (b) He should draw a side by side boxplot of heart beat and weight and a scatterplot of smoking and marital status.
+: (b) He should draw a side by side box plot of heart beat and weight and a scatter plot of smoking and marital status.
-: (c) He should draw a side by side boxplot of smoking and marital status and a segmented bar chart of hear beat and weight.
+: (c) He should draw a side by side box plot of smoking and marital status and a segmented bar chart of heart beat and weight.
-: (d) He should draw a back to back stem and leaf plot of weight and heart beat and examine the cell frequencies in the contingency table for smoking by marital status.
+: (d) He should draw a back-to-back stem and leaf plot of weight and heart beat and examine the cell frequencies in the contingency table for smoking by marital status.
-* As part of an experiment in perception, 160 University of Michigan psych students completed a task on identifying similar objects. On average, the students spent 8.25 minutes with sa tandard deviation of 2.4 minutes. However, the minimum time was 2.3 minutes and one students worked for almost 60 minutes. What is the best description of the histogram of times that students spent on this task? Choose one answer.
+* As part of an experiment in perception, 160 University of Michigan psychology students completed a task on identifying similar objects. On average, the students spent 8.25 minutes with standard deviation of 2.4 minutes. However, the minimum time was 2.3 minutes and one students worked for almost 60 minutes. What is the best description of the histogram of times that students spent on this task? Choose one answer.
 : (a) The histogram of times could be symmetrical and not normal with major outliers.
-: (b) The histogram of times could be left skewed, and in case there are any outliers, it is likely that they will be smaller than the mean.
+: (b) The histogram of times could be left skewed, and if there are any outliers, they will likely be smaller than the mean.
-: (c) The histogram of times could be right skewed, and in the case of any outliers, it is likely that they will be larger than the mean.
+: (c) The histogram of times could be right skewed, and if there are any outliers, they will likely be larger than the mean.
 : (d) The histogram of times could be normal with no major outliers.

@@ Line 88: / Line 88: @@
 ====Mixed quantitative and qualitative methods====
-Suppose we want to design and complete a comparative longitudinal study using mixed methods to examine an intervention for recurrent depression.  We want to use 2 patient groups for the real intervention and 2 groups with a sham intervention, each meeting once a week for three weeks.  To determine if the intervention is effective, we plan to collect data pre- and post-intervention on 10 variables such as self-efficacy, social support, quality of life, depression rating, etc. at 4 time points (baseline, prior to commencing the study, at the conclusion of the 3 weeks of intervention, 3 months out and chronically, 6 months after the completion of the study).
+Suppose we want to design and complete a comparative longitudinal study using mixed methods to examine an intervention for recurrent depression.  We want to use 2 patient groups for the real intervention and 2 groups with a sham intervention, each meeting once a week for three weeks.  To determine if the intervention is effective, we plan to collect data pre- and post-intervention on 10 variables (including self-efficacy, social support, quality of life, depression rating, etc.) at 4 time points (first before commencing the study, then at the conclusion of the 3 weeks of intervention, then again 3 months out, and once more 6 months after the completion of the study).
-[http://obssr.od.nih.gov/scientific_areas/methodology/mixed_methods_research/section2.aspx Mixed methods] for combining quantitative with qualitative data analyses may be used in such cases. These techniques contextualize quantitative statistical inference with methods from the qualitative interviewing process. Our primary analytical focus is to obtain inferences regarding factors (e.g., subject phenotypes) that may be associated with observed outcomes (e.g., depression) and treatments (e.g., intervention/sham). The statistical inference in this context involves conclusions about the entire study population and stratified cohorts based on various trait characteristics. Exploratory and graphical summaries of relevant numerical data may support such quantitative statistical inference. Both statistical significances (e.g., effect-sizes) and clinical differences (e.g., depression scale rating) in parent-related outcomes may be studied continuously starting at baseline (before intervention) to the chronic state (6 months after intervention). For instance, when examining continuous variables, an effect size may be calculated and assessed using [[AP_Statistics_Curriculum_2007_Infer_2Means_Indep#Effect-Sizes|Cohen's d]]. Effect sizes of 0.1–0.3 are generally considered small, whereas effect sizes of 0.3–0.5 and ≥ 0.5 are considered to be medium and large, respectively. For secondary outcomes, such as dichotomized adherence, the [[SMHS_OR_RR|odds ratios (ORs)]] may be calculated to report the effect size. The OR is the ratio of the odds of an outcome (e.g., being adherent) of one group (e.g., baseline) to the odds of the outcome of another group (chronic, post 6th month follow-up). OR-derived effect-sizes may be classified as small ($1.5<OR<2.5$), medium ($2.5<OR<4.3$), or high ($OR>4.3$). Frequency and percentage representation of nominal categorical data will be used and medians and quartiles (Q1–Q3) may be calculated for continuous, non-normally distributed variables.
+[http://obssr.od.nih.gov/scientific_areas/methodology/mixed_methods_research/section2.aspx Mixed methods] for combining quantitative with qualitative data analyses may be used in such cases. These techniques contextualize quantitative statistical inference with methods from the qualitative interviewing process. Our primary analytical focus is to obtain inferences regarding factors (e.g., subject phenotypes) that may be associated with observed outcomes (e.g., depression) and treatments (e.g., intervention/sham). The statistical inference in this context involves conclusions about the entire study population and stratified cohorts based on various trait characteristics. Exploratory and graphical summaries of relevant numerical data may support such quantitative statistical inference. Both statistical significances (e.g., effect-sizes) and clinical differences (e.g., depression scale rating) in parent-related outcomes may be studied continuously, starting at the ''baseline'' (before the intervention) and ending at the ''chronic'' state (6 months after intervention). When examining continuous variables, for instance, an effect size may be calculated and assessed using [[AP_Statistics_Curriculum_2007_Infer_2Means_Indep#Effect-Sizes|Cohen's d]]. Effect sizes of 0.1–0.3 are generally considered small, whereas effect sizes of 0.3–0.5 and ≥ 0.5 are considered to be medium and large, respectively. For secondary outcomes, such as dichotomized adherence, the [[SMHS_OR_RR|odds ratios (ORs)]] may be calculated to report the effect size. The OR is the ratio of the odds of an outcome (e.g., being adherent) of one group (e.g., the baseline) to the odds of the outcome of another group (the chronic, post-6th month follow-up). OR-derived effect-sizes may be classified as small ($1.5<OR<2.5$), medium ($2.5<OR<4.3$), or high ($OR>4.3$). Frequency and percentage representation of nominal categorical data will be used and medians and quartiles (Q1–Q3) may be calculated for continuous, non-normally distributed variables.
 ===Software ===