5

You do not have the data to directly answer the question if a ride for a given service is new customer or old customer. You need to have a customer id to properly attribute if there is new growth or just service switching. Any statistics you run will, at best, show a correlation between new service and increased revenue. There can be no causation ...


4

It is not a case of paired nominal data. Hence, Mc Nemar's test can not be applied to check whether there is a higher fatality rate in Philippines ?. THE fatality rate is given for Philippines and world ( excluding Philippines ). As defined, it is expressed as proportion. Therefore, t-test/z-test shall be appropriate given that you meet other conditions ...


3

You could try carrying out some form of hypothesis testing. Null Hypothesis: Mean sales of the standard product in a day $ = \mu$. Alternate Hypothesis: Mean sales of the standard product in a day $ \neq \mu$ You could then extract the rows where ride type is standard and split it into 2 time periods - before and after the premium services were introduced. ...


2

As I know we have below equation for Ridge Regression: \begin{equation} RSS_{Ridge} = \Sigma_{i=1}^{n} (\hat{y}_{i} - y_{i})^2 - \lambda \Sigma_{j=1}^{p}(\beta^2) \end{equation} First of all, it seems to me, if lambda goes higher does not mean that coefficients go down with inverse relation to lambda. Because the power of beta is two and the lambda is one. ...


1

For a beginner, I would say the SIR model is a great place to start: https://idmod.org/docs/general/model-sir.html Numberphile did a great video on using SIR to predict Covid-19: https://www.youtube.com/watch?v=k6nLfCbAzgo Hopefully this can get you started on your journey!


1

Control charts are widely used still (whether people know it or not). However it is limited to cases where it is possible to define meaningful and stable targets and bounds for a metric. The classical example is in manufacturing, when the intent is to produce items which has a certain property at a certain value. Like each cereal box produced should be 510 ...


1

It looks like a good case for ANOVA, which can be seen as a generalization of Student t-test for more than two groups. For visualizing the differences I'd suggest using boxplots (or violin plots) for every group: it's more informative than only plotting the mean which is sensitive to outliers.


1

1) I have two classes (Admitted & Not-admitted) 2) Around 25 input variables 3) Run a logistic regression (Statsmodel logit or Scikit-learn?) Do we always have to predict the outcome class to know the risk factors that lead to admission/hospitalization? 5) Then identify the significant risk factors based on p-value. Not necessary, you can just perform ...


1

I believe you can use a classification algorithm where you manually overrepresent the "anomalies" class. By how much, depends on the cost induced by the anomalies. Just to illustrate what I mean: Anomalies cover a continuum between two extremes: 1) those which can be safely ignored, because they induce no costs, and 2) those whose costs is unbearable. As ...


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