# Cause and effect relationship clue words in math

### Glossary of Key Words | NSW Education Standards

Cause & Effect. Cause is why something happened. Effect is what happened. Signal Words. Cause & Effect. • So. • Because. • Since. • If Then • Therefore. For material that shows cause and effect, you need to anticipate the linkage and note the relationship. The importance of these patterns is that they signal how. several causes and/or effects. Sometimes clue words such as since, as a result , caused, thus, therefore, because, if, then, so, and consequently are used to show cause-and-effect relationships. Ghost man · Hovercraft Racing · Quick Math.

Once students have mastered the art of the persuasive discussion, students will relay these ideas onto the page in the form of persuasive writing.

### Organizational Patterns of a Paragraph

The year will end with a brief revision on Narratives. Students will revisit this topic and learn to write about the varying genres in the narrative format. Our writers will develop their stamina with extended writing periods that will give them a taste of what to expect in Year 6.

Mathematics This term, Year 5 will continue to develop their understanding of the Measurement and Geometry and Statistics and Probability units. We will begin the term by focusing on geometric angles and the importance of their use in the real world. We will provide investigative activities which will encourage students to build on their prior knowledge to develop deeper insight to the units.

Students will have the opportunity to find the area and perimeter of regular and rectilinear shapes using their ability to measure using appropriate units. In the Statistics and Probability units, students will be exposed to a range of graphs and charts and will be charged with interpreting them through deep thought and collaboration. Students will learn to analyse chance and probability through fun and engaging games.

Throughout our lessons, students will continue to be encouraged to use critical thinking and clear explanation to solve open-ended problems relevant to their lives. Integrated Studies This term we will complete a Biological Science unit on animal adaptations. We will be visiting Werribee Zoo to investigate and observe first hand the adaptations different animals have that enable them to survive in their environment. Developing quantum extensions of causal models, however, has proven challenging because of the peculiar features of quantum mechanics.

For instance, if two or more quantum systems are entangled, it is hard to deduce whether statistical correlations between them imply a cause-effect relationship. Historically, statisticians thought that all information about a system could be represented in terms of statistical correlations among its variables.

Nowadays, however, it is recognized that the concept of causal information goes beyond that of correlation.

## Reading Comprehension

Knowing there are more cars, I can infer that the air is more polluted. Similarly, knowing the air is more polluted, I can infer that there are more cars. The causal statement tells us more; namely, if we change the number of cars, we can affect air pollution, but not vice versa—polluting the air by other means say by building factories will not affect the number of cars.

Causal information is different from correlations because it tells us how the system changes under interventions. This principle states that two correlated variables must have a common cause: In the latter case, the correlation will disappear if probabilities are conditioned to the common cause.

For example, the incidence of tsunamis in Chile is statistically correlated with that of tsunamis in Japan. In statistical terms, the combined probability for two tsunamis is greater than the product of the separate probabilities for tsunamis in Chile and Japan. But neither event is a cause of the other.

In other words, the correlation disappears. The post is a little technically detailed at points. However, the first three sections of the post are non-technical, and I hope will be of broad interest. You may find it informative to work through these exercises and problems. Before diving in, one final caveat: I am not an expert on causal inference, nor on statistics. The reason I wrote this post was to help me internalize the ideas of the causal calculus.

Occasionally, one finds a presentation of a technical subject which is beautifully clear and illuminating, a presentation where the author has seen right through the subject, and is able to convey that crystalized understanding to others.

Nonetheless, I hope others will find my notes useful, and that experts will speak up to correct any errors or misapprehensions on my part. You might think that we could conclude from this that being Republican, rather than Democrat, was an important factor in causing someone to vote for the Civil Rights Act.

However, the picture changes if we include an additional factor in the analysis, namely, whether a legislator came from a Northern or Southern state. If we include that extra factor, the situation completely reverses, in both the North and the South. Democrat 94 percentRepublican 85 percent South: Democrat 7 percentRepublican 0 percent Yes, you read that right: You might wonder how this can possibly be true.

You can skip the numbers if you trust my arithmetic. In fact, at the time the House had 94 Democrats, and only 10 Republicans.

The numbers above are for the House of Congress. The numbers were different in the Senate, but the same overall phenomenon occurred.

If we take a naive causal point of view, this result looks like a paradox. As I said above, the overall voting pattern seems to suggest that being Republican, rather than Democrat, was an important causal factor in voting for the Civil Rights Act.

So two variables which appear correlated can become anticorrelated when another factor is taken into account.

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- English Language Arts
- Identify Signal Words

You might wonder if results like those we saw in voting on the Civil Rights Act are simply an unusual fluke. But, in fact, this is not that uncommon.

In each case, understanding the causal relationships turns out to be much more complex than one might at first think. Imagine you suffer from kidney stones, and your Doctor offers you two choices: Your Doctor tells you that the two treatments have been tested in a trial, and treatment A was effective for a higher percentage of patients than treatment B.

Keep in mind that this really happened.