A.5 Answer: TW 5 tutorial

A few issues: Five decimal places is to the nearest \(0.01\) of a mm! The standard deviation of the difference is not the difference between the individual standard deviations. A standard deviation cannot be negative. (Same applies to standard errors, but we aren't there yet.) Note that there is a sample size of \(0\) for the difference!
A few issues: Five decimal places: That's accuracy to \(0.00001\) of a millimetre per second (I don't think so...). There is no numerical measures of the most important thing and the thing the RQ (presumably) concerns: The differences between the two brands.

A few issues: vertical axis is not labelled (presumably burn time in seconds); horizontal axis is not labelled (we have no way of knowing what is happening there.)
A few issues: this is not a summary: this shows the burn-time of every individual candle, and makes it hard to compare means (which is the RQ); why is every bar labelled, which just adds unnecessary clutter; fonts are hard too read (small); a boxplot (or dotchart) would be the appropriate graph. In addition: the largest value is over \(70\) mins! Do a quick project!
A few issues: Compares just two numbers (means) using lots of ink; no indication of variation in the data; a boxplot (or dotchart) would be appropriate.

Plot 1: \(0.94\) (correlation D); Plot 2: \(-0.95\) (correlation A); Plot 3: \(0.12\) (correlation B); Plot 6: \(0.75\) (correlation C).
Correlation is inappropriate for Plot 4 (non-linear) and Plot 5 (non-linear).
Examples of the direction in Plot 1: any two variables moderately positively correlated, such as height and weight, distance lived from university and travel time, etc.
Examples of direction in Plot 2: any two variables moderately negatively correlated, such as hours of weekly exercise and body weight, number of SCI110 tutorial missed and final mark, etc.
Plot 1: \(88.4\)%; Plot 2: \(90.3\)%; Plot 3: \(1.4\)%; Plot 6: \(56.3\)%.

Answers implied by H5P.

Five variables. ('Participants' would not be summarised, it is technically an identifier and not a variable, as each person has a unique value).
Age; Height; Weight and quantitaive continuous.
Gender (nominal; two levels); GMFCS (ordinal; three levels)
As follows:
- 'Gender': Percentages (or number) F and M
- 'Age': Mean/median; standard deviation/IQR
- 'Height': Mean/median; standard deviation/IQR
- 'Weight': Mean/median; standard deviation/IQR
- 'GMFCS': Percentages (or numbers) in each group
As follows:
- 'Gender': Barchart (not really needed)
- 'Age': Histogram/stemplot
- 'Height': Histogram/stemplot
- 'Weight': Histogram/stemplot
- 'GMFCS': Barchart/piechart
As follows:
- Between Gender and Height: Boxplot
- Between Gender and GMFCS: Side-by-side or stacked bar chart.

Relational.
Observational: no intervention
O: average accuracy. Response variable: the 'accuracy' for each individual.
C: Between blind and sighted individuals. Explanatory variable: Whether the individual is blind, or not.
No intervention: the C (whether someone is blind or not) cannot be manipulated by the researchers.
Quantitative continuous.
Qualitative nominal.
A boxplot.
The unit of analysis is the person.
\(34 + 36 = 70\).
One: each person gets one 'accuracy' value.

Not shown.
The median temperatures similar; a slight increase from Office A to C. IQR similar for each office, and range similar for Offices A and C (slightly larger for Office B). In summary: Office A a bit different (cooler) on average.
Perhaps Office A.

Stacked or side-by-side barcharts; whether bird injured or not, and whether upper or lower are both qualitative variables.
Boxplots would be OK; one quantitative (length-of-stay) and one qualitative (therapy type) variable.
Scatterplots; two quantitative variables (frequency; amount wheat consumed).
Two-way table or stacked/side-by-side barchart, or table; two qualitative variables vars (\(30\) mins of physical activity or not; vigorous physical

Answers implied by H5P.

Explanatory variable is the number of hours of light each plant received; response variable is the number of flowers each plant received. The units of observation are the individual plants.
Explanatory variable is the number of smokers at the restaurant; response variable is the concentration of fine particulate matter.
Response variable is the waiting time; explanatory variable is the time of admission.
Explanatory variable is the "Level of Playfulness"; response variable is the "Level of Coping".

To display the relationship between the age of athletes, and the times taken for each athlete to complete the `beep test': Scatterplot
To display the percentage of elderly people in nursing homes that can independently complete a given task, for each of the seven tasks on a given list: Bar chart or Dot chart
To display the relationship between the type of fertiliser applied to a vegetable crop (either A, B or C) and the yield per hectare, measured over numerous plots: Boxplot
To display the distribution of the hardness measurements taken of 250 samples of bamboo: Histogram
To display the relationship between the age of concrete test cylinders (under \(5\) years; \(5\) years and older) and their strength (fit for purpose; or not fit for purpose): Side-by-side or stacked bar chart

Observational; the ant nest would be pre-existing.
Both are 'ant nests'.
Looks good!
Uphill looks different to the other two. Also: the lower dots comprise mostly blue dots (Sourdough Trail), so perhaps a difference between locations too.

True experiment: treatments given (experiment), and the researchers selected the groups, groups did not previously existed (so true experiment).
Observation: each patient. Analysis: The patient.
Pretty good. A label on the horizontal axis would be better, but the caption does contain that information.
Pain is reduced.
Yes, probably. We cannot be sure due to randomness...

Boxplot; scatterplot.