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clarify exercise and remove solution
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exercises/11-tipr-exercises.qmd

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## Your turn
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Use the `tip_coef()` function to conduct a sensitivity analysis for the estimate from your previous exercises.
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Use the `tip_coef()` function to conduct a sensitivity analysis for the estimate from your previous exercises. Use the lower bound of the confidence interval for the effect and `0.1` for the exposure-confounder effect.
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```{r}
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slides/raw/11-tipr.qmd

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### `tip_coef()`
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* `effect_observed`: observed exposure - outcome effect **4.45 minutes (95% CI: 0.17, 8.37)**
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* `effect_observed`: observed exposure - outcome effect **4.32 minutes (95% CI: 0.0009, 8.36)**
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## Disney Data
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### `tip_coef()`
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* `confounder_outcome_effect`: relationship between the unmeasured confounder and outcome
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## Disney Data
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### `tip_coef()`: **specify** one, it will **estimate** the other
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::: {.nonincremental}
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* `exposure_confounder_effect`
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* `confounder_outcome_effect`
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:::
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## Disney Data
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*Our causal effect estimate: **4.45 minutes (95% CI: 0.17, 8.37)***
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::: small
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```{r}
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#| eval: true
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#| echo: false
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#| output-location: fragment
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library(tipr)
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tip_coef(
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effect_observed = 0.17,
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exposure_confounder_effect = 0.1,
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verbose = FALSE
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)
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```
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:::
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```{r}
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#| eval: false
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#| output-location: fragment
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library(tipr)
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tip_coef(
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effect_observed = 0.17,
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exposure_confounder_effect = 0.1
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)
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```
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## *Your turn*
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```{r}
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countdown::countdown(minutes = 5)
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```
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### Use the `tip_coef()` function to conduct a sensitivity analysis for the estimate from your previous exercises.
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### Use the `tip_coef()` function to conduct a sensitivity analysis for the estimate from your previous exercises. Use the lower bound of the confidence interval for the effect and `0.1` for the exposure-confounder effect.

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