Counts with multiple criteria

There are times when you will need to summarize data based on multiple criteria. You can use the AVERAGEIFS(), COUNTIFS(), and SUMIFS() functions to specify multiple criteria. Recall that these functions take pairs of ranges and criteria to include only the cells that meet the specified conditions.

Now you'll tally the number of crimes that occur after 8:00 in the evening. A couple of reminders:

Remember that the time data are stored in 24-hour format.
You can specify comparison arguments by including the operator in the criterion (e.g., "<900").

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Error and Uncertainty in Google Sheets

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In cell I2, count the number of crimes that occurred in the West precinct after 8:00 (remember that 8:00 is 2000 in 24-hour format).
Adapt this function for cells I3:I7 to count the number of evening crimes in each of the remaining precincts.

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Error and Uncertainty in Google Sheets

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The first chapter presents common terminology, introduces methods for determining significant differences between groups, and outlines the kinds of error and uncertainty involved. We will specifically look at Seattle crime data and evaluate crime rate differences between precincts and neighborhoods. This chapter will equip learners to identify threats to the validity and accuracy of their conclusions.

Exercise 1: Defining error and uncertainty Exercise 2: Measures of central tendency Exercise 3: Crime time Exercise 4: IF functions Exercise 5: Extracting UNIQUE() values Exercise 6: Book 'em and count 'em Exercise 7: Averages and IF conditions Exercise 8: Counts with multiple criteria

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Exercise 9: Correlation Exercise 10: Rap sheet Exercise 11: Correlation preparation Exercise 12: A (crimes) committed relationship Exercise 13: Strong relationships

The second chapter outlines both rudimentary (e.g., moving average, seasonal average, yearly average) and more complicated methods (e.g., linear regression) for making predictions and outlines the kinds of error and uncertainty involved. We will specifically look at anonymized student grades data and evaluate the accuracy of our predictions for given students. Throughout the chapter, we will identify threats to the validity and accuracy of our predictions.

Exercise 1: Making the grade Exercise 2: We all have our (central) tendencies Exercise 3: Variable weights Exercise 4: Now weight a minute Exercise 5: Lying in weights Exercise 6: Advanced prediction strategies Exercise 7: What's in the FORECAST()?Exercise 8: Variation in predictions Exercise 9: Seems about right Exercise 10: How clear is your crystal ball?Exercise 11: Prediction accuracy Exercise 12: Absolute deviation Exercise 13: Average absolute deviation Exercise 14: Statistical significance Exercise 15: Significant differences Exercise 16: Significant differences of opinion

Chapter 3 encourages learners to test the assumptions of their predictions using data on car crashes. Specifically, they will determine how to allocate resources to reduce injuries and fatalities from auto accidents. Learners will discuss the impact of outliers in prediction accuracy, evaluate the importance of normally distributed data in making predictions, employ consequence-likelihood matrices in risk management, and adapt psychological heuristics to discussions of numerical uncertainty and risk.

Exercise 1: Outliers Exercise 2: Down and outlier Exercise 3: No filter Exercise 4: Addressing outliers Exercise 5: Sparklines Exercise 6: Can't start a fire without a spark(line)Exercise 7: The max matters Exercise 8: What's the worst that could happen?Exercise 9: There are consequences Exercise 10: A likely story Exercise 11: Risky business Exercise 12: Risky business Exercise 13: Random numbers Exercise 14: How random Exercise 15: Be fruitful and multiply Exercise 16: Revisiting sparklines

The final chapter integrates all the previous lessons into a constructed-world scenario. Learners are tasked with updating the menu at their small business: the Risky Business Bakery. They need to figure out whether to add or drop menu items based on whether there are significant differences in sales by baked good; whether their predicted sales figures from their accountant are accurate.

Exercise 1: A half-baked idea?Exercise 2: Half-baked ideas Exercise 3: Falling on hearth times Exercise 4: Summary statistics Exercise 5: Do you know your muffins, man?Exercise 6: Changing prices Exercise 7: Bread on the rise?Exercise 8: Paying the price Exercise 9: Is change on the menu?Exercise 10: A recipe for change Exercise 11: Rain, rain, go away Exercise 12: Fed up Exercise 13: Review: Are we certain now?Exercise 14: Adding variation Exercise 15: Win some, lose some Exercise 16: Just t-testing Exercise 17: Wrap-up