Contents
What is the relationship between temperature and ice cream sales?
We can see that the variable temperature are positively correlated with ice-cream sales (r=0.600). This means that when the daily temperature increases the ice-cream sales also increases.
Does an increase in temperature cause an increase in ice cream sales?
The weather and ice cream sales As temperatures rise ice cream sales increase. There is a correlation between the temperature and ice cream sales. At first sight this may appear to be a direct relationship. However ice cream sales don’t automatically occur as the temperature rises.
What type of relationship exists between the sales of ice creams is increasing so do the sales of sunglasses?
Example: Correlation between Ice cream sales and sunglasses sold. As the sales of ice creams is increasing so do the sales of sunglasses. Causation takes a step further than correlation.
What type of relationship between ice cream sales and temperature is shown on the scatter graph?
The scatter plot shows no relationship between ice cream sales and temperature. The scatter plot shows during low temperatures there are high ice cream sales.
Is more ice cream sold in winter or summer?
More ice cream is sold during the winter months than summer months.
How much ice cream is sold per year?
This is encouraging, since sales of ice cream have remained relatively flat over the last several years. Overall sales for the ice cream industry in the United States has remained around $10 billion annually since 2010. In 2013, sales were $13.7 billion.
Is there a relationship between ice cream sales and temp?
This means there is a strong correlation between ice cream sales and temp. So our y-intercept demonstrates many points. For example: When x is 60 degrees, y is $332. the slope is 13.006 . This numer shows how steep the relationship of our variables is.
What is the relation between k-means clustering and PCA?
In theorem 2.2 they state that if you do k-means (with k=2) of some p-dimensional data cloud and also perform PCA (based on covariances) of the data, then all points belonging to cluster A will be negative and all points belonging to cluster B will be positive, on PC1 scores.
When do you use PCA in feature selection?
PCA is used for dimensionality reduction / feature selection / representation learning e.g. when the feature space contains too many irrelevant or redundant features. The aim is to find the intrinsic dimensionality of the data. Here’s a two dimensional example that can be generalized to higher dimensional spaces.